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| 1 | A Survey on Sentiment and Emotion Analysis for Computational Literary Studies | 1 | A Survey on Sentiment and Emotion Analysis for Computational Literary Studies |
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| 3 | <div style="margin: 1em 0 1em 0;">Evgeny Kim <a href="javascript:switchlayer('author1');"><img style="margin-left: 1%" src="/sites/default/files/arrow-down.png" alt="Autoreninformationen"></a></div> | 4 | <div style="margin: 1em 0 1em 0;">Evgeny Kim <a href="javascript:switchlayer('author1');"><img style="margin-left: 1%" src="/sites/default/files/arrow-down.png" alt="Autoreninformationen"></a></div> |
| 4 | <div style="display:none; width:100%; background-color: #fafafa;" id="author1"> Kontakt: <a href="mailto:evgeny.kim@ims.uni-stuttgart.de">evgeny.kim@ims.uni-stuttgart.de</a><br>Institution: Universität Stuttgart, Institut für Maschinelle Sprachverarbeitung <br>GND: <a href=" http://d-nb.info/gnd/1193672481" target="_blank:">1193672481</a><br>ORCID: <a href="https://orcid.org/0000-0001-6822-6709" target="_blank:">0000-0001-6822-6709</a><br></div> | 5 | <div style="display:none; width:100%; background-color: #fafafa;" id="author1"> Kontakt: <a href="mailto:evgeny.kim@ims.uni-stuttgart.de">evgeny.kim@ims.uni-stuttgart.de</a><br>Institution: Universität Stuttgart, Institut für Maschinelle |
| 6 | Sprachverarbeitung <br>GND: <a href=" http://d-nb.info/gnd/1193672481" target="_blank:">1193672481</a><br>ORCID: <a href="https://orcid.org/0000-0001-6822-6709" target="_blank:">0000-0001-6822-6709</a><br></div> | ||
| 5 | <div style="margin: 1em 0 1em 0;">Roman Klinger <a href="javascript:switchlayer('author2');"><img style="margin-left: 1%" src="/sites/default/files/arrow-down.png" alt="Autoreninformationen"></a></div> | 7 | <div style="margin: 1em 0 1em 0;">Roman Klinger <a href="javascript:switchlayer('author2');"><img style="margin-left: 1%" src="/sites/default/files/arrow-down.png" alt="Autoreninformationen"></a></div> |
| 6 | <div style="display:none; width:100%; background-color: #fafafa;" id="author2"> Kontakt: <a href="mailto:roman.klinger@ims.uni-stuttgart.de">roman.klinger@ims.uni-stuttgart.de</a><br>Institution: Universität Stuttgart, Institut für Maschinelle Sprachverarbeitung <br>GND: <a href=" http://d-nb.info/gnd/173873820" target="_blank:">173873820</a><br>ORCID: <a href="https://orcid.org/0000-0002-2014-6619" target="_blank:">0000-0002-2014-6619</a><br></div> | 8 | <div style="display:none; width:100%; background-color: #fafafa;" id="author2"> Kontakt: <a href="mailto:roman.klinger@ims.uni-stuttgart.de">roman.klinger@ims.uni-stuttgart.de</a><br>Institution: Universität Stuttgart, Institut für Maschinelle |
| 9 | Sprachverarbeitung <br>GND: <a href=" http://d-nb.info/gnd/173873820" target="_blank:">173873820</a><br>ORCID: <a href="https://orcid.org/0000-0002-2014-6619" target="_blank:">0000-0002-2014-6619</a><br></div> | ||
| 7 | </div> | 10 | </div> |
| 9 | <hr class="one"> | 12 | <hr class="one"> |
| 10 | <p><span class="bolder">DOI: </span><a href="http://dx.doi.org/10.17175/2019_008">10.17175/2019_008</a></p> | 13 | <p><span class="bolder">DOI: </span><a href="http://dx.doi.org/10.17175/2019_008_v2">10.17175/2019_008_v2</a></p> |
| 11 | <p><span class="bolder">Nachweis im OPAC der Herzog August Bibliothek: </span><a href="http://opac.lbs-braunschweig.gbv.de/DB=2/XMLPRS=N/PPN?PPN= | 14 | <p><span class="bolder">Nachweis im OPAC der Herzog August Bibliothek: </span><a href="http://opac.lbs-braunschweig.gbv.de/DB=2/XMLPRS=N/PPN?PPN=176443949X " target="_blank">176443949X</a></p> |
| 12 | <p><span class="bolder">Erstveröffentlichung: </span>16.12.2019 | 15 | <p><span class="bolder">Erstveröffentlichung: </span>16.12.2019 |
| 13 | </p> | 16 | </p> |
| 17 | <p><span class="bolder">Version 2.0: </span>23.07.2021 | ||
| 18 | </p | ||
| 14 | <p><span class="bolder">Lizenz:</span> Sofern nicht anders angegeben <a href="https://creativecommons.org/licenses/by-sa/4.0/" rel="license" target="_blank"><img alt="Creative Commons Lizenzvertrag" src="/themes/zfdg/images/licensebuttons/l/by-sa/4.0/80x15.png"><br></a></p> | 19 | <p><span class="bolder">Lizenz:</span> Sofern nicht anders angegeben <a href="https://creativecommons.org/licenses/by-sa/4.0/" rel="license" target="_blank"><img alt="Creative Commons Lizenzvertrag" src="/themes/zfdg/images/licensebuttons/l/by-sa/4.0/80x15.png"><br></a></p> |
| 16 | <div id="info2"> | 21 | <div id="info2"> |
| 17 | <p><span class="bolder">Medienlizenzen: </span>Medienrechte liegen bei den | 22 | <p><span class="bolder">Medienlizenzen: </span>Medienrechte liegen bei den Autor*innen |
| 18 | </p> | 23 | </p> |
| 19 | <p><span class="bolder">Letzte Überprüfung aller Verweise: </span> | 24 | <p><span class="bolder">Letzte Überprüfung aller Verweise: </span>22.07.2021 |
| 20 | </p> | 25 | </p> |
| 22 | </p> | 27 | </p> |
| 23 | <p><span class="bolder">Empfohlene Zitierweise: </span>Evgeny Kim, Roman Klinger: A Survey on Sentiment and Emotion Analysis for Computational Literary Studies. In: Zeitschrift für digitale Geisteswissenschaften. Wolfenbüttel 2019. text/html Format. DOI: <a href="http://dx.doi.org/10.17175/2019_008">10.17175/2019_008</a></p> | 28 | <p><span class="bolder">Empfohlene Zitierweise: </span>Evgeny Kim, Roman Klinger: A Survey on Sentiment and Emotion Analysis for |
| 29 | Computational Literary Studies. In: Zeitschrift für digitale Geisteswissenschaften. Erstveröffentlichung vom 16.12.2019. Version 2.0 vom 23.07.2021. Wolfenbüttel 2021. text/html Format. DOI: <a href="http://dx.doi.org/10.17175/2019_008_v2">10.17175/2019_008_v2</a></p> | ||
| 24 | <hr class="one"> | 30 | <hr class="one"> |
| 26 | </div> | 32 | </div> |
| 27 | <div class="content"> | 33 | <div class="content"><a name="div2"> </a><div id="abstract"> |
| 28 | <div id="abstract_en" class="abstract"> | 34 | <div id="abstract_de" class="abstract"> |
| 29 | <h1>Abstract</h1> | 35 | <h1>Abstract</h1>Emotionen sind ein wesentlicher Bestandteil fesselnder Erzählungen: |
| 30 | <p>Emotions are a crucial part of compelling narratives: literature tells us about | 36 | Literatur erzählt uns von Menschen mit Zielen, Wünschen, Leidenschaften |
| 31 | people with goals, desires, passions, and intentions. In the past, the | 37 | und Absichten. Die Analyse von Emotionen ist Teil des breiteren und |
| 32 | affective dimension of literature was mainly studied in the context of literary | 38 | größeren Feldes der Sentimentanalyse und findet in der |
| 33 | hermeneutics. However, with the emergence of the research field known as | 39 | Literaturwissenschaft zunehmend Beachtung. In der Vergangenheit wurde |
| 34 | <span style="color:#035151"><i>Digital Humanities</i></span> (DH), some studies of emotions in a literary context have | 40 | die affektive Dimension der Literatur hauptsächlich im Rahmen der |
| 35 | taken a computational turn. Given the fact that DH is still being formed as a | 41 | literarischen Hermeneutik untersucht. Mit dem Aufkommen der Digital |
| 36 | field, this direction of research can be rendered relatively new. In this | 42 | Humanities (DH) als Forschungsfeld, haben jedoch einige Studien über |
| 37 | survey, we offer an overview of the existing body of research on sentiment and | 43 | Emotionen im literarischen Kontext eine computergestützte Wendung |
| 38 | emotion analysis as applied to literature. The research under review deals with | 44 | genommen. In Anbetracht der Tatsache, dass sich die DH als Feld noch im |
| 39 | a variety of topics including tracking dramatic changes of a plot development, | 45 | Aufbau befindet, kann diese Forschungsrichtung als relativ neu |
| 40 | network analysis of a literary text, and understanding the emotionality of | 46 | bezeichnet werden. In dieser Übersicht bieten wir einen Überblick über |
| 41 | texts, among other topics. | 47 | die bestehende Forschung zur Emotionsanalyse in der Literatur. Die |
| 42 | </p> | 48 | untersuchte Forschungsliteratur befasst sich mit einer Vielzahl von |
| 49 | Themen, darunter die Veränderungen der emotionalen Konnotation im | ||
| 50 | Verlauf eines Texts, die Netzwerkanalyse eines literarischen Textes und | ||
| 51 | das Verstehen der Emotionalität von Texten, neben anderen Themen. | ||
| 52 | Basierend auf diesem Überblick weisen wir auf eine Reihe von | ||
| 53 | verbleibenden Herausforderungen hin, die vielversprechende zukünftige | ||
| 54 | Forschungsrichtungen darstellen. | ||
| 43 | </div> | 55 | </div> |
| 44 | <hr class="one"> | 56 | <hr class="one"> |
| 45 | <div id="abstract_ | 57 | <div id="abstract_en" class="abstract">Emotions are a crucial part of compelling narratives: literature tells us |
| 46 | 58 | about people with goals, desires, passions, and intentions. Emotion | |
| 47 | | 59 | analysis is part of the broader and larger field of sentiment analysis, |
| 48 | | 60 | and receives increasing attention in literary studies. In the past, the |
| 49 | | 61 | affective dimension of literature was mainly studied in the context of |
| 50 | | 62 | literary hermeneutics. However, with the emergence of the research field |
| 51 | (DH) | 63 | known as Digital Humanities (DH), some studies of emotions in a literary |
| 52 | | 64 | context have taken a computational turn. Given the fact that DH is still |
| 53 | | 65 | being formed as a field, this direction of research can be rendered |
| 54 | | 66 | relatively new. In this survey, we offer an overview of the existing |
| 55 | | 67 | body of research on emotion analysis as applied to literature. The |
| 56 | | 68 | research under review deals with a variety of topics including tracking |
| 57 | | 69 | dramatic changes of a plot development, network analysis of a literary |
| 58 | | 70 | text, and understanding the emotionality of texts, among other topics. |
| 59 | | 71 | Based on this review, we point to a set of remaining challenges that |
| 60 | | 72 | constitute promising future research directions. |
| 61 | </div> | 73 | </div> |
| 62 | <hr class="one"> | 74 | <hr class="one"> |
| 63 | <div>Zu diesem Artikel ist eine überarbeitete Version erschienen: <a href="https://zfdg.de/2019_008">Version 2</a></div> | 75 | <div id="versionsbox"> |
| 64 | <hr class="one"> | 76 | <h3>Version 2.0 (05.07.2021)</h3> |
| 77 | <p>Es wurden folgende Änderungen vorgenommen: Inhaltliche Anpassungen, wie sie von | ||
| 78 | den Gutachten angemerkt worden sind. Austausch der Tab. 1. Aktualisierung und Ergänzung | ||
| 79 | der | ||
| 80 | bibliographischen Angaben. Formale Korrekturen. | ||
| 81 | </p> | ||
| 82 | </div> | ||
| 65 | <div id="headings"><br><br><hr class="two"><br><ul> | 83 | <div id="headings"><br><br><hr class="two"><br><ul> |
| 66 | <li><a href="#hd1">1 Introduction and Motivation</a></li> | 84 | <li><a href="#hd1">1 Introduction and Motivation</a></li> |
| 67 | <li><a href="#hd2">1.1 Emotions and Arts</a></li> | 85 | <li><a href="#hd2">1.1 Scope of this Survey</a></li> |
| 68 | <li><a href="#hd3">2 Affect and Emotion</a></li> | 86 | <li><a href="#hd3">1.2 Emotion Analysis and Digital Humanities</a></li> |
| 69 | <li><a href="#hd4">2.1 Ekman’s Theory of Basic Emotions</a></li> | 87 | <li><a href="#hd4">1.3 Emotions and Arts</a></li> |
| 70 | <li><a href="#hd5">2.2 Plutchik’s Wheel of Emotions</a></li> | 88 | <li><a href="#hd5">2 Affect and Emotion</a></li> |
| 71 | <li><a href="#hd6">2.3 Russel’s Circumplex Model</a></li> | 89 | <li><a href="#hd6">2.1 Ekman’s Theory of Basic Emotions</a></li> |
| 72 | <li><a href="#hd7">3 Emotion Analysis in Non-computational Literary Studies</a></li> | 90 | <li><a href="#hd7">2.2 Plutchik’s Wheel of Emotions</a></li> |
| 73 | <li><a href="#hd8">4 Emotion and Sentiment Analysis in Computational Literary Studies</a></li> | 91 | <li><a href="#hd8">2.3 Russel’s Circumplex Model </a></li> |
| 74 | <li><a href="#hd9">4.1 Emotion Classification</a></li> | 92 | <li><a href="#hd9">3 Emotion Analysis in Non-computational Literary Studies</a></li> |
| 75 | <li><a href="#hd10">4.1.1 Classification based on emotions</a></li> | 93 | <li><a href="#hd10">4 Emotion and Sentiment Analysis in Computational Literary Studies</a></li> |
| 76 | <li><a href="#hd11">4.1.2 Classification of happy ending vs. non-happy endings</a></li> | 94 | <li><a href="#hd11">4.1 Emotion Classification</a></li> |
| 77 | <li><a href="#hd12">4.2 Genre and Story-type Classification</a></li> | 95 | <li><a href="#hd12">4.1.1 Classification based on emotions</a></li> |
| 78 | <li><a href="#hd13">4.2.1 Story-type clustering</a></li> | 96 | <li><a href="#hd13">4.1.2 Classification of happy ending vs. non-happy |
| 79 | <li><a href="#hd14">4.2.2 Genre classification</a></li> | 97 | endings</a></li> |
| 80 | <li><a href="#hd15">4.3 Temporal Change of Sentiment</a></li> | 98 | <li><a href="#hd14">4.2 Genre and Story-type Classification</a></li> |
| 81 | <li><a href="#hd16">4.3.1 Topography of emotions</a></li> | 99 | <li><a href="#hd15">4.2.1 Story-type clustering</a></li> |
| 82 | <li><a href="#hd17">4.3.2 Tracking sentiment</a></li> | 100 | <li><a href="#hd16">4.2.2 Genre classification</a></li> |
| 83 | <li><a href="#hd18">4.3.3 Sentiment recognition in historical texts</a></li> | 101 | <li><a href="#hd17">4.3 Structural Changes of Sentiment</a></li> |
| 84 | <li><a href="#hd19">4.4 Character Network Analysis and Relationship Extraction</a></li> | 102 | <li><a href="#hd18">4.3.1 Topography of emotions</a></li> |
| 85 | <li><a href="#hd20">4.4.1 Sentiment dynamics between characters</a></li> | 103 | <li><a href="#hd19">4.3.2 Tracking sentiment</a></li> |
| 86 | <li><a href="#hd21">4.4.2 Character analysis and character relationships</a></li> | 104 | <li><a href="#hd20">4.3.3 Sentiment recognition in historical |
| 87 | <li><a href="#hd22">4.5 Other Types of Emotion Analysis</a></li> | 105 | texts</a></li> |
| 88 | <li><a href="#hd23">4.5.1 Emotion flow analysis and visualization</a></li> | 106 | <li><a href="#hd21">4.4 Character Network Analysis and |
| 89 | <li><a href="#hd24">4.5.2 Miscellaneous</a></li> | 107 | Relationship Extraction</a></li> |
| 90 | <li><a href="#hd25">5 Discussion and Conclusion</a></li> | 108 | <li><a href="#hd22">4.4.1 Sentiment dynamics between |
| 91 | <li><a href="#hd26">Acknowledgements</a></li> | 109 | characters</a></li> |
| 92 | <li><a href="#hd27">Bibliographic References</a></li> | 110 | <li><a href="#hd23">4.4.2 Character analysis and character |
| 93 | <li><a href="#hd28">List of Figures with Captions</a></li> | 111 | relationships</a></li> |
| 112 | <li><a href="#hd24">4.5 Other Types of Emotion Analysis</a></li> | ||
| 113 | <li><a href="#hd25">4.5.1 Emotion flow analysis and | ||
| 114 | visualization</a></li> | ||
| 115 | <li><a href="#hd26">4.5.2 Miscellaneous</a></li> | ||
| 116 | <li><a href="#hd27">5 Discussion and Conclusion</a></li> | ||
| 117 | <li><a href="#hd28">Acknowledgements</a></li> | ||
| 118 | <li><a href="#hd29">Bibliographic References</a></li> | ||
| 119 | <li><a href="#hd30">List of Figures with Captions</a></li> | ||
| 94 | </ul> | 120 | </ul> |
| 95 | </div><br><div id="chapter"><a name="hd1"> </a><h2> | 121 | </div><br></div><a name="div3"> </a><div id="chapter"><a name="hd1"> </a><h2> |
| 96 | <div style="position:relative;width:90%;">1 Introduction and Motivation</div> | 122 | <div style="position:relative;width:90%;">1 Introduction and Motivation</div> |
| 97 | </h2> | 123 | </h2> |
| 98 | <p>This article deals with <i>emotion</i> and <i>sentiment</i> analysis in <span style="color:#035151"><i>computational literary studies</i></span>. | 124 | <p id="pid1"><span style="float: right; padding: .2em; color: #999; border: 1px solid #ccc; background-color: #eee; margin-right: -80px;">[<a href="#pid1">1</a>]</span>This article deals with <i>emotion</i> and <i>sentiment</i> analysis in <span style="color:#035151"><i>computational literary |
| 99 | Following Liu<a id="fna1" class="fn" href="#fn1" title="Liu 2015, p.2.">[1]</a>, we define sentiment as a | 125 | studies</i></span>. Following Liu,<a id="fna1" class="fn" href="#fn1" title="Liu 2015, p. 2.">[1]</a> we define sentiment as a <i>positive</i> or |
| 100 | <i>positive</i> or <i>negative</i> feeling | 126 | <i>negative</i> feeling underlying the opinion. |
| 101 | underlying the opinion. The term <i>opinion</i> in this sense is | 127 | Sometimes, sentiment analysis is interpreted synonymously to opinion mining, |
| 102 | close to <i>attitude</i> in psychology and both sentiment analysis | 128 | however strictly speaking, opinion mining is an application that makes use |
| 103 | and opinion mining are often used interchangeably. Sentiment analysis is an area of | 129 | of sentiment analysis and contextualizes polarity ratings in topics, aspects |
| 104 | computational linguistics that analyzes people’s sentiments and opinions regarding | 130 | and targets. Though sentiment analysis is primarily text-oriented, there are |
| 105 | different objects or topics. Though sentiment analysis is primarily text-oriented, | 131 | multimodal approaches as well.<a id="fna2" class="fn" href="#fn2" title="Soleymani et al. 2017.">[2]</a></p> |
| 106 | there are multimodal approaches as well.<a id="fna2" class="fn" href="#fn2" title="Soleymani et al. 2017.">[2]</a></p> | 132 | <p id="pid2"><span style="float: right; padding: .2em; color: #999; border: 1px solid #ccc; background-color: #eee; margin-right: -80px;">[<a href="#pid2">2</a>]</span>Another interpretation of the term <i>sentiment analysis</i> |
| 107 | <p>Defining the concept of <i>emotion</i> is a challenging task. As | 133 | is as broader description of a research field, which considers affective |
| 108 | Scherer puts it, defining emotion is a notorious problem.<a id="fna3" class="fn" href="#fn3" title="Scherer 2005, p. 695.">[3]</a> Indeed, different methodological and conceptual | 134 | computing applied to textual analysis. In this sense, it also includes the |
| 109 | approaches to dealing with emotions lead to different definitions. However, the | 135 | distinction into subjective or objective statements,<a id="fna3" class="fn" href="#fn3" title="Wiebe et al. 2004.">[3]</a> and, more recently, the field of emotion |
| 110 | majority of emotion theorists agree that emotions involve a set of expressive, | 136 | analysis.Defining the concept of <i>emotion</i> is a |
| 111 | behavioral, physiological, and phenomenological features.<a id="fna4" class="fn" href="#fn4" title="Scarantino 2016, p. 36.">[4]</a> In this view, an emotion can be defined as an | 137 | challenging task. As Scherer puts it, »defining emotion is a notorious |
| 112 | integrated feeling state involving physiological changes, motor-preparedness, | 138 | problem«.<a id="fna4" class="fn" href="#fn4" title="Scherer 2005, p. 1.">[4]</a> |
| 113 | cognitions about action, and inner experiences that emerges from an appraisal of the | 139 | Indeed, different methodological and conceptual approaches to dealing with |
| 114 | self or situation.<a id="fna5" class="fn" href="#fn5" title="Mayer et al. 2008, p. 510.">[5]</a></p> | 140 | emotions lead to different definitions. However, the majority of emotion |
| 115 | <p>Similar to sentiment, emotions can be analyzed computationally. However, the goal | 141 | theorists agree that emotions involve a set of expressive, behavioral, |
| 116 | of | 142 | physiological, and phenomenological features.<a id="fna5" class="fn" href="#fn5" title="Scarantino 2016, p. 36.">[5]</a> In this view, an emotion can be defined |
| 117 | emotion analysis is to recognize the emotion, rather than sentiment, which makes it | 143 | as »an integrated feeling state involving physiological changes, |
| 118 | a | 144 | motor-preparedness, cognitions about action, and inner experiences that |
| 119 | more difficult task as differences between emotions are subtler than those between | 145 | emerges from an appraisal of the self or situation«.<a id="fna6" class="fn" href="#fn6" title="Mayer et al. 2008, p. 2.">[6]</a></p> |
| 120 | positive and negative. | 146 | <p id="pid3"><span style="float: right; padding: .2em; color: #999; border: 1px solid #ccc; background-color: #eee; margin-right: -80px;">[<a href="#pid3">3</a>]</span>Similar to sentiment, emotions can be analyzed computationally. However, the |
| 121 | </p> | 147 | goal of emotion analysis is to recognize the emotion, rather than sentiment, |
| 122 | <p>Although sentiment and emotion analysis are different tasks, our review of the | 148 | which makes it a more difficult task as differences between some emotion |
| 123 | literature shows that the use of either term is not always consistent. There are | 149 | classes are more subtle than those between positive and negative. |
| 124 | cases where researchers analyze only positive and negative aspects of a text but | 150 | </p> |
| 125 | refer to their analysis as emotion analysis. Likewise, there are cases where | 151 | <p id="pid4"><span style="float: right; padding: .2em; color: #999; border: 1px solid #ccc; background-color: #eee; margin-right: -80px;">[<a href="#pid4">4</a>]</span>Although sentiment and emotion analysis are different tasks, our review of |
| 126 | researchers look into a set of subjective feelings including emotions but call it | 152 | the literature shows that the use of either term is not always consistent. |
| 127 | sentiment analysis. Hence, to avoid confusion, in this survey, we use the terms <span style="color:#035151"><i>emotion analysis</i></span> and <span style="color:#035151"><i>sentiment analysis</i></span> | 153 | There are cases where researchers analyze only positive and negative aspects |
| 128 | interchangeably. In most cases, we follow the terminology used by the authors of the | 154 | of a text but refer to their analysis as emotion analysis. Likewise, there |
| 129 | papers we discuss (i.e., if they call emotions sentiments, we do the same). | 155 | are cases where researchers look into a set of subjective feelings including |
| 130 | </p> | 156 | emotions but call it sentiment analysis. Hence, to avoid confusion, in this |
| 131 | <p>Finally, we talk about sentiment and emotion analysis in the context of computational | 157 | survey, we use the terms <span style="color:#035151"><i>emotion analysis</i></span> and <span style="color:#035151"><i>sentiment analysis</i></span> interchangeably. In most cases, we |
| 132 | literary studies. Da defines computational literary studies as the statistical | 158 | follow the terminology used by the authors of the papers we discuss (i.e., |
| 133 | representation of patterns discovered in text mining fitted to currently existing | 159 | if they call emotions sentiments, we do the same). However, our focus of |
| 134 | knowledge about literature, literary history, and textual production.<a id="fna6" class="fn" href="#fn6" title="Da 2019, p. 602.">[6]</a> Computational literary studies are | 160 | this survey is on emotion analysis, and we do not include the majority of |
| 135 | synonymous to <span style="color:#035151"><i>distant reading</i></span><a id="fna7" class="fn" href="#fn7" title="Moretti 2005.">[7]</a> and <span style="color:#035151"><i>digital | 161 | work that focuses on binary polarities. |
| 136 | literary studies</i></span>,<a id="fna8" class="fn" href="#fn8" title="Hoover et al. 2014.">[8]</a> | 162 | </p> |
| 137 | each of which refers to the practice of running a textual analysis on a computer to | 163 | <p id="pid5"><span style="float: right; padding: .2em; color: #999; border: 1px solid #ccc; background-color: #eee; margin-right: -80px;">[<a href="#pid5">5</a>]</span>Finally, we talk about sentiment and emotion analysis in the context of |
| 138 | yield quantitative results. In this survey, we use all of these terms interchangeably | 164 | computational literary studies. Da defines computational literary studies as |
| 139 | and when we refer to digital humanities as a field, we refer to those groups of | 165 | the statistical representation of patterns discovered in text mining fitted |
| 140 | researchers whose primary objects of study are texts. | 166 | to currently existing knowledge about literature, literary history, and |
| 141 | </p> | 167 | textual production.<a id="fna7" class="fn" href="#fn7" title="Da 2019, p. 602.">[7]</a> |
| 142 | <div id="subchapter"><a name="hd2"> </a><h3> | 168 | Computational literary studies are closely related to the concepts of <span style="color:#035151"><i>distant reading</i></span><a id="fna8" class="fn" href="#fn8" title="Moretti 2005.">[8]</a> and <span style="color:#035151"><i>digital |
| 143 | <div style="position:relative;width:90%;">1.1 Emotions and Arts</div> | 169 | literary studies</i></span>,<a id="fna9" class="fn" href="#fn9" title="Hoover et al. 2014.">[9]</a> each of which refers to the practice of running a textual |
| 170 | analysis on a computer to yield quantitative results. In this survey, we use | ||
| 171 | all of these terms interchangeably and when we refer to digital humanities | ||
| 172 | as a field, we refer to those groups of researchers whose primary objects of | ||
| 173 | study are texts. | ||
| 174 | </p><a name="div4"> </a><div id="subchapter"><a name="hd2"> </a><h3> | ||
| 175 | <div style="position:relative;width:90%;">1.1 Scope of this Survey</div> | ||
| 144 | </h3> | 176 | </h3> |
| 145 | <p>Much of our daily experiences influence and are influenced by the emotions we | 177 | <p id="pid6"><span style="float: right; padding: .2em; color: #999; border: 1px solid #ccc; background-color: #eee; margin-right: -80px;">[<a href="#pid6">6</a>]</span>This survey provides an overview of work which aims at understanding or |
| 146 | experience.<a id="fna9" class="fn" href="#fn9" title="Schwarz 2000, p. 433.">[9]</a> This experience is | 178 | analyzing emotions in literature. We include studies that answer a |
| 147 | not limited to real events. People can feel emotions because they are reading a novel | 179 | concrete research question from the field of literary studies with |
| 148 | or watching a play or a movie.<a id="fna10" class="fn" href="#fn10" title="Johnson-Laird / Oatley 2016, passim; Djikic et al. 2009, passim.">[10]</a> There is a growing | 180 | computational methods. We do only consider publications in English that |
| 149 | body of literature that pinpoints the importance of emotions for literary comprehension, | 181 | have been quality-assessed by peer review (except for few exceptions). |
| 150 | <a id="fna11" class="fn" href="#fn11" title="Robinson 2005; Hogan 2010; Hogan 2011; Bal / Veltkamp 2013; Djikic et al. 2013; Johnson 2012; Samur et al. 2018.">[11]</a> as well as research | 182 | We exclude efforts of corpus creation and annotation, if those corpora |
| 151 | that recognizes the deliberate choices people make with regard to their emotional | 183 | have not been used for a further research study to limit the scope of |
| 152 | states when seeking narrative enjoyment such as a book or a film<a id="fna12" class="fn" href="#fn12" title="Zillmann et al. 1980; Ross 1999; Bryant / Zillmann 1984; Oliver 2008; Mar et al. 2011.">[12]</a> | 184 | this survey (though such work is clearly relevant and important) and |
| 153 | The link between emotions and arts in general is a matter of debate that dates back | 185 | software development efforts if the associated papers do not aim at |
| 154 | to the Ancient period, particularly to Plato, who viewed passions and desires as the | 186 | contributing to answering a research question. Similarly, we do mostly |
| 155 | lowest kind of knowledge and treated poets as undesirable members in his ideal | 187 | exclude reports on ongoing research efforts, if they do not contribute a |
| 156 | society.<a id="fna13" class="fn" href="#fn13" title="Plato 1969 , passim.">[13]</a> In contrast, Aristotle’s | 188 | novel understanding of a research question. Our literature research |
| 157 | view on emotive components of poetry expressed in his <i>Poetics</i><a id="fna14" class="fn" href="#fn14" title="Aristotle 1996, passim.">[14]</a> differed from Plato’s in that | 189 | started in the field of computational linguistics with the <a href="https://www.aclweb.org/anthology/" target="_blank">ACL Anthology</a> and |
| 158 | emotions do have great importance, particularly in the moral life of a person.<a id="fna15" class="fn" href="#fn15" title="De Sousa / Scarantino 2018.">[15]</a> In the late nineteenth | 190 | has been complemented by other research that cites such papers or is |
| 159 | century the emotion theory of arts stepped into the spotlight of philosophers. One | 191 | cited by them. We exclude papers from local digital humanities |
| 160 | of | 192 | conferences. |
| 161 | the first accounts on the topic is given by Leo Tolstoy in 1898 in his essay <i>What is Art?</i>.<a id="fna16" class="fn" href="#fn16" title="Tolstoy 1962, passim.">[16]</a> Tolstoy argues that art | 193 | </p> |
| 162 | can express emotions experienced in fictitious context and the degree to which the | 194 | <p id="pid7"><span style="float: right; padding: .2em; color: #999; border: 1px solid #ccc; background-color: #eee; margin-right: -80px;">[<a href="#pid7">7</a>]</span>The goal of this survey is to provide an overview of recent methods of |
| 163 | audience is convinced of them defines the success of the artistic work.<a id="fna17" class="fn" href="#fn17" title="Anderson / McMaster 1986, p. 3; Hogan 2010, p. 187; Piper / Jean So 2015.">[17]</a></p> | 195 | emotion and sentiment analysis as applied to a text. The survey is |
| 164 | <p>New methods of quantitative research emerged in humanities scholarship bringing forth | 196 | directed at researchers looking for an introduction to the existing |
| 165 | the so-called <i>digital revolution</i><a id="fna18" class="fn" href="#fn18" title="Lanham 1989.">[18]</a> and the transformation of the | 197 | research in the field of sentiment and emotion analysis of a (primarily, |
| 166 | field into what we know as digital humanities.<a id="fna19" class="fn" href="#fn19" title="Berry 2012; Schreibman et al. 2015.">[19]</a> The adoption of computational | 198 | literary) text. We do not not cover applications of emotion analysis in |
| 167 | methods of text analysis and data mining from the fields of then fast-growing areas | 199 | the areas of digital humanities that are not focused on text. Neither do |
| 168 | of computational linguistics and artificial intelligence provided humanities scholars | 200 | we provide an in-depth overview of all possible applications of emotion |
| 169 | with new tools of text analytics and data-driven approaches to theory | 201 | analysis in the computational context outside of the DH line of |
| 170 | formulation.<a id="fna20" class="fn" href="#fn20" title="Vanhoutte 2013, p. 142; Jockers / Underwood 2016, pp. 292f.">[20]</a></p> | 202 | research. |
| 171 | <p>To the best of our knowledge, the first work<a id="fna21" class="fn" href="#fn21" title="Anderson / McMaster 1982.">[21]</a> on a computer-assisted modeling of emotions in | 203 | </p> |
| 172 | literature appeared in 1982. Challenged by the question of why some texts are more | 204 | </div><a name="div5"> </a><div id="subchapter"><a name="hd3"> </a><h3> |
| 173 | interesting than others, Anderson and McMaster concluded that the emotional tone of | 205 | <div style="position:relative;width:90%;">1.2 Emotion Analysis and Digital Humanities</div> |
| 174 | a | 206 | </h3> |
| 175 | story can be responsible for the reader’s interest. The results of their study | 207 | <p id="pid8"><span style="float: right; padding: .2em; color: #999; border: 1px solid #ccc; background-color: #eee; margin-right: -80px;">[<a href="#pid8">8</a>]</span>Methods that apply emotion analysis can in general be categorized |
| 176 | suggest that a large-scale analysis of the emotional tone of a collection of texts | 208 | into (<a title="" href="#hd1">section 1</a>) dictionary-based methods, (<a title="" href="#hd5">chapter 2</a>) feature-based |
| 177 | is | 209 | machine-learning-based, and (<a title="" href="#hd9">section 3</a>) representation-learning/deep |
| 178 | possible with the help of a computer program. There are two implications of this | 210 | learning-based. Methods that apply statistical learning (<a title="" href="#hd8">section 2.3</a>) to |
| 179 | finding. First, they suggested that by identifying emotional tones of text passages | 211 | induce a model that takes text as input and output predictions rely |
| 180 | one can model affective patterns of a given text or a collection of texts, which in | 212 | in the majority of cases (in this field) on supervised approaches – |
| 181 | turn can be used to challenge or test existing literary theories. Second, their | 213 | a learning algorithm is presented with annotated data and needs to |
| 182 | approach to affect modeling demonstrates that the stylistic properties of texts can | 214 | output a model that can, as good as possible on unseen data, do such |
| 183 | be defined on the basis of their emotional interest and not only their linguistic | 215 | predictions. These approaches have advantages: The learner can |
| 184 | characteristics. With regard to these implications, this work is an important early | 216 | exploit (long-distant) dependencies between textual units, learn |
| 185 | piece as it laid out a roadmap for some of the basic applications of sentiment and | 217 | associations between semanic meaning and concepts to learn, and make |
| 186 | emotion analysis of texts, namely sentiment and emotion pattern recognition from text | 218 | use of semantic similarities between words; even those that have not |
| 187 | and computational text characterization based on sentiment and emotion. | 219 | been seen in training data. This comes at a cost – the need for |
| 188 | </p> | 220 | annotated data. The situation between the fields of computational |
| 189 | <p>With the development of research methods used by digital humanities researchers, the | 221 | linguistics and digital humanities differs substantially in this |
| 190 | number of approaches and goals of emotion and sentiment analysis in literature has | 222 | regard. |
| 191 | grown. The goal of this survey is to provide an overview of these recent methods of | 223 | </p> |
| 192 | emotion and sentiment analysis as applied to a text. The survey is directed at | 224 | <p id="pid9"><span style="float: right; padding: .2em; color: #999; border: 1px solid #ccc; background-color: #eee; margin-right: -80px;">[<a href="#pid9">9</a>]</span>The focus in computational linguistics is to develop methods to solve |
| 193 | researchers looking for an introduction to the existing research in the field of | 225 | a particular task – analyze syntax, respresent semantics, or develop |
| 194 | sentiment and emotion analysis of a (primarily, literary) text. The survey does not | 226 | well-performing classification methods, for instance for emotion |
| 195 | cover applications of emotion and sentiment analysis in the areas of digital | 227 | classification. Therefore, there exists a substantial body of |
| 196 | humanities that are not focused on text. Neither does it provide an in-depth overview | 228 | research on natural language processing which is essentially |
| 197 | of all possible applications of emotion analysis in the computational context outside | 229 | agnostic to the corpus. In fact, a method is typically evaluated on |
| 198 | of the DH line of research. | 230 | a set of different resources to prove its generalizability, and even |
| 199 | </p> | 231 | if a novel corpus is presented for future studies, this is compared |
| 200 | </div> | 232 | to existing resources. This comes with an advantage: Resources are |
| 201 | </div> | 233 | often built by domain experts, which are then used for further |
| 202 | <div id="chapter"><a name="hd3"> </a><h2> | 234 | analysis; the diversity might be limited, but is often sufficient |
| 235 | for model development. | ||
| 236 | </p> | ||
| 237 | <p id="pid10"><span style="float: right; padding: .2em; color: #999; border: 1px solid #ccc; background-color: #eee; margin-right: -80px;">[<a href="#pid10">10</a>]</span>In digital humanities, this situation differs substantially. The goal | ||
| 238 | is often not the development of a computational model that is able | ||
| 239 | to make predictions for the entirety of a field (which is of course | ||
| 240 | also not achieved in computational linguistics, but that is | ||
| 241 | sometimes claimed to be a goal). Instead, the object of research (a | ||
| 242 | particular text, a genre, an author, ...) is of higher importance. | ||
| 243 | This comes with a challenge: Annotators often need to be experts in | ||
| 244 | the particular domain, for a particular object of research. | ||
| 245 | </p> | ||
| 246 | <p id="pid11"><span style="float: right; padding: .2em; color: #999; border: 1px solid #ccc; background-color: #eee; margin-right: -80px;">[<a href="#pid11">11</a>]</span>That might be the reason, as we will see, that, in contrast to | ||
| 247 | research in computational linguistics, using lexicons of words | ||
| 248 | associated with the concepts of interest, receives some attention as | ||
| 249 | a methodological approach to emotion analysis. This comes at the | ||
| 250 | cost of accuracy, as such methods are (mostly) not able to interpret | ||
| 251 | the context appropriately (with some exceptions which embed | ||
| 252 | dictionaries with rules<a id="fna10" class="fn" href="#fn10" title="E.g. Shaikh 2009.">[10]</a>), however, it contributes the advantage of being transparent | ||
| 253 | not only with the predictions and the results, but also with the | ||
| 254 | analysis algorithm. | ||
| 255 | </p> | ||
| 256 | </div><a name="div6"> </a><div id="subchapter"><a name="hd4"> </a><h3> | ||
| 257 | <div style="position:relative;width:90%;">1.3 Emotions and Arts</div> | ||
| 258 | </h3> | ||
| 259 | <p id="pid12"><span style="float: right; padding: .2em; color: #999; border: 1px solid #ccc; background-color: #eee; margin-right: -80px;">[<a href="#pid12">12</a>]</span>Much of our daily experiences influence and are influenced by the emotions we | ||
| 260 | experience.<a id="fna11" class="fn" href="#fn11" title="Schwarz 2000, p. 433.">[11]</a> This experience is not limited to real events. People can | ||
| 261 | feel emotions because they are reading a novel or watching a play or a | ||
| 262 | movie.<a id="fna12" class="fn" href="#fn12" title="Johnson-Laird / Oatley 2016; Djikic et al. 2009.">[12]</a> | ||
| 263 | There is a growing | ||
| 264 | body of literature that pinpoints the importance of emotions for | ||
| 265 | literary comprehension,<a id="fna13" class="fn" href="#fn13" title="Robinson 2005; Hogan 2010; Hogan 2011; Bal / Veltkamp 2013; Djikic et al. 2013; Johnson 2012; Samur et al. 2018.">[13]</a> | ||
| 266 | as well as | ||
| 267 | research that recognizes the deliberate choices people make with | ||
| 268 | regard to their emotional states when seeking narrative | ||
| 269 | enjoyment such as a book or a film.<a id="fna14" class="fn" href="#fn14" title="Zillmann et al. 1980; Ross 1999; Bryant / Zillmann 1984; Oliver 2008; Mar et al. 2011.">[14]</a></p> | ||
| 270 | <p id="pid13"><span style="float: right; padding: .2em; color: #999; border: 1px solid #ccc; background-color: #eee; margin-right: -80px;">[<a href="#pid13">13</a>]</span>The link between emotions and arts in general is a matter of | ||
| 271 | debate that dates back to the Ancient period, particularly to | ||
| 272 | Plato, who viewed passions and desires as the lowest kind of | ||
| 273 | knowledge and treated poets as undesirable members in his ideal | ||
| 274 | society.<a id="fna15" class="fn" href="#fn15" title="Plato 1969.">[15]</a> In | ||
| 275 | contrast, Aristotle’s view on emotive components of poetry | ||
| 276 | expressed in his <i>Poetics</i><a id="fna16" class="fn" href="#fn16" title="Aristotle 1996.">[16]</a> differed from | ||
| 277 | Plato’s in that emotions do have great importance, particularly | ||
| 278 | in the moral life of a person.<a id="fna17" class="fn" href="#fn17" title="de Sousa / Scarantino 2018.">[17]</a> In the late nineteenth century the | ||
| 279 | emotion theory of arts stepped into the spotlight of | ||
| 280 | philosophers. One of the first accounts on the topic is given by | ||
| 281 | Leo Tolstoy in 1898 in his essay <i>What is Art?</i>.<a id="fna18" class="fn" href="#fn18" title="Tolstoy 1962.">[18]</a> Tolstoy | ||
| 282 | argues that art can express emotions experienced in fictitious | ||
| 283 | context and the degree to which the audience is convinced of | ||
| 284 | them defines the success of the artistic work.<a id="fna19" class="fn" href="#fn19" title="Anderson / McMaster 1986, p. 3; Hogan 2010, p. 187; Piper / Jean So 2015.">[19]</a></p> | ||
| 285 | <p id="pid14"><span style="float: right; padding: .2em; color: #999; border: 1px solid #ccc; background-color: #eee; margin-right: -80px;">[<a href="#pid14">14</a>]</span>New methods of quantitative research emerged in humanities | ||
| 286 | scholarship bringing forth the so-called <i>digital revolution</i><a id="fna20" class="fn" href="#fn20" title="Lanham 1989.">[20]</a> and the | ||
| 287 | transformation of the field into what we know as digital | ||
| 288 | humanities.<a id="fna21" class="fn" href="#fn21" title="Berry 2012; Schreibman et al. 2015.">[21]</a> The adoption of computational methods of | ||
| 289 | text analysis and data mining from the fields of then | ||
| 290 | fast-growing areas of computational linguistics and artificial | ||
| 291 | intelligence provided humanities scholars with new tools of text | ||
| 292 | analytics and data-driven approaches to theory formulation.<a id="fna22" class="fn" href="#fn22" title="Vanhoutte 2013, p. 142; Jockers / Underwood 2016, pp. 292f.">[22]</a></p> | ||
| 293 | <p id="pid15"><span style="float: right; padding: .2em; color: #999; border: 1px solid #ccc; background-color: #eee; margin-right: -80px;">[<a href="#pid15">15</a>]</span>To the best of our knowledge, the first work<a id="fna23" class="fn" href="#fn23" title="Anderson / McMaster 1982.">[23]</a> on a computer-assisted | ||
| 294 | modeling of emotions in literature appeared in 1982. Challenged | ||
| 295 | by the question of why some texts are more interesting than | ||
| 296 | others, Anderson and McMaster concluded that the | ||
| 297 | »emotional tone« of a story can be responsible | ||
| 298 | for the reader’s interest. The results of their study suggest | ||
| 299 | that a large-scale analysis of the »emotional tone« | ||
| 300 | of a collection of texts is possible with the help of a computer | ||
| 301 | program. There are two implications of this finding. First, they | ||
| 302 | suggested that by identifying emotional tones of text passages | ||
| 303 | one can model affective patterns of a given text or a collection | ||
| 304 | of texts, which in turn can be used to challenge or test | ||
| 305 | existing literary theories. Second, their approach to affect | ||
| 306 | modeling demonstrates that the stylistic properties of texts can | ||
| 307 | be defined on the basis of their emotional interest and not only | ||
| 308 | their linguistic characteristics. With regard to these | ||
| 309 | implications, this work is an important early piece as it laid | ||
| 310 | out a roadmap for some of the basic applications of sentiment | ||
| 311 | and emotion analysis of texts, namely sentiment and emotion | ||
| 312 | pattern recognition from text and computational text | ||
| 313 | characterization based on sentiment and emotion. | ||
| 314 | </p> | ||
| 315 | <p id="pid16"><span style="float: right; padding: .2em; color: #999; border: 1px solid #ccc; background-color: #eee; margin-right: -80px;">[<a href="#pid16">16</a>]</span>With the development of research methods used by digital | ||
| 316 | humanities researchers, the number of approaches and goals of | ||
| 317 | emotion and sentiment analysis in literature has grown. | ||
| 318 | </p> | ||
| 319 | </div> | ||
| 320 | </div><a name="div7"> </a><div id="chapter"><a name="hd5"> </a><h2> | ||
| 203 | <div style="position:relative;width:90%;">2 Affect and Emotion</div> | 321 | <div style="position:relative;width:90%;">2 Affect and Emotion</div> |
| 204 | </h2> | 322 | </h2> |
| 205 | <p>The history of emotion research has a long and rich tradition that followed Darwin’s | 323 | <p id="pid17"><span style="float: right; padding: .2em; color: #999; border: 1px solid #ccc; background-color: #eee; margin-right: -80px;">[<a href="#pid17">17</a>]</span>The history of emotion research has a long and rich tradition that followed |
| 206 | 1872 publication of <i>The Expression of the Emotions in Man and Animals</i><a id="fna22" class="fn" href="#fn22" title="Darwin 1872, passim.">[22]</a>. The subject of emotion theories is vast | 324 | Darwin’s 1872 publication of <i>The Expression of the Emotions in Man and |
| 207 | and diverse. We refer the reader to Maria Gendron’s paper<a id="fna23" class="fn" href="#fn23" title="Gendron / Feldman Barrett 2009.">[23]</a> for a brief history of ideas about emotion | 325 | Animals</i>.<a id="fna24" class="fn" href="#fn24" title="Darwin 1872.">[24]</a> The subject of emotion theories |
| 208 | in psychology. Here, we will focus on three views on emotion that are popular in | 326 | is vast and diverse. We refer the reader to Maria Gendron’s paper<a id="fna25" class="fn" href="#fn25" title="Gendron / Feldman Barrett 2009.">[25]</a> for a brief |
| 209 | computational analysis of emotions: Ekman’s <span style="color:#035151"><i>theory of basic | 327 | history of ideas about emotion in psychology. Here, we will focus on three |
| 210 | emotions</i></span>, Plutchik’s <span style="color:#035151"><i>wheel of emotion</i></span>, and Russel’s | 328 | views on emotion that are popular in computational analysis of emotions |
| 211 | <span style="color:#035151"><i>circumplex model</i></span>. | 329 | (though they are, from a psychological perspective, motivated from different |
| 212 | </p> | 330 | perspectives and represent different elements of affect and emotion): |
| 213 | <div id="subchapter"><a name="hd4"> </a><h3> | 331 | Ekman’s <span style="color:#035151"><i>theory of basic emotions</i></span>, Plutchik’s <span style="color:#035151"><i>wheel of emotion</i></span>, and Russel’s <span style="color:#035151"><i>circumplex model</i></span>. |
| 332 | </p><a name="div8"> </a><div id="subchapter"><a name="hd6"> </a><h3> | ||
| 214 | <div style="position:relative;width:90%;">2.1 Ekman’s Theory of Basic Emotions</div> | 333 | <div style="position:relative;width:90%;">2.1 Ekman’s Theory of Basic Emotions</div> |
| 215 | </h3> | 334 | </h3> |
| 216 | <p>The basic emotion theory was first articulated by Silvan Tomkins<a id="fna24" class="fn" href="#fn24" title="Tomkins 1962, passim.">[24]</a> in the early 1960s. Tomkins postulated that each instance | 335 | <p id="pid18"><span style="float: right; padding: .2em; color: #999; border: 1px solid #ccc; background-color: #eee; margin-right: -80px;">[<a href="#pid18">18</a>]</span>The idea of basic emotion theories is that there are emotions that are |
| 217 | of a certain emotion is biologically similar to other instances of the same emotion | 336 | more "fundamental" than others. Mixtures of emotions which receive a |
| 218 | or shares a common trigger. One of Tomkins’ mentees, Paul Ekman, put in question the | 337 | particular name are not necessarily defined as being basic. Attempts to |
| 219 | existing emotion theories that proclaimed that facial expressions of emotion are | 338 | find a definition for emotions date back to Silvan Tomkins<a id="fna26" class="fn" href="#fn26" title="Tomkins 1962.">[26]</a> in the early 1960s, who |
| 220 | socially learned and therefore vary from culture to culture. Ekman, Sorenson and | 339 | characterized emotions based on similarities of stimuli and biological |
| 221 | Friesen challenged this view<a id="fna25" class="fn" href="#fn25" title="Ekman et al. 1969, pp. 86-88.">[25]</a> | 340 | processes, following the ideas that have been described already by |
| 222 | in a field study with the outcome that facial displays of fundamental emotions are | 341 | Charles Darwin – clearly an attempt that focuses on observations and |
| 223 | not learned but innate. However, there are culture-specific prescriptions about how | 342 | evolution. |
| 224 | and in which situations emotions are displayed. | 343 | </p> |
| 225 | </p> | 344 | <p id="pid19"><span style="float: right; padding: .2em; color: #999; border: 1px solid #ccc; background-color: #eee; margin-right: -80px;">[<a href="#pid19">19</a>]</span>One of Tomkins’ mentees, Paul Ekman, put in question the existing emotion |
| 226 | <p>Based on the observation of facial behavior in early development or social | 345 | theories that proclaimed that facial expressions of emotion are socially |
| 227 | interaction, Ekman’s theory also postulates that emotions should be considered <span style="color:#035151"><i>discrete categories</i></span><a id="fna26" class="fn" href="#fn26" title="Ekman 1993, p. 386.">[26]</a> | 346 | learned and therefore vary from culture to culture. Ekman, Sorenson and |
| 228 | rather than continuous. Though this | 347 | Friesen challenged this view<a id="fna27" class="fn" href="#fn27" title="Ekman et al. 1969, pp. 86–88.">[27]</a> in a field study with the outcome that facial |
| 229 | view allows for conceiving of emotions as having different intensities, it does not | 348 | displays of fundamental emotions are not learned but innate. However, |
| 230 | allow emotions to blend and leaves no room for more complex affective states in which | 349 | there are culture-specific prescriptions about how and in which |
| 231 | individuals report the <span style="color:#035151"><i>co-occurrence of like-valenced discrete | 350 | situations emotions are displayed. Based on the observation of facial |
| 232 | emotions</i></span>.<a id="fna27" class="fn" href="#fn27" title="Feldman Barrett 1998, pp. 580f.">[27]</a> This and other theory | 351 | behavior in early development or social interaction, Ekman’s theory also |
| 233 | postulates were widely criticized and disputed in literature.<a id="fna28" class="fn" href="#fn28" title="Russell 1994; Russell et al. 2003; Gendron et al. 2014; Feldman Barrett 2017.">[28]</a></p> | 352 | postulates that emotions should be considered <span style="color:#035151"><i>discrete |
| 234 | </div> | 353 | categories</i></span><a id="fna28" class="fn" href="#fn28" title="Ekman 1993, p. 386.">[28]</a> rather than |
| 235 | <div id="subchapter"><a name="hd5"> </a><h3> | 354 | continuous. Though this view allows for conceiving of emotions as having |
| 355 | different intensities, it does not allow emotions to blend and leaves no | ||
| 356 | room for more complex affective states in which individuals report the | ||
| 357 | <span style="color:#035151"><i>co-occurrence of like-valenced discrete | ||
| 358 | emotions</i></span>.<a id="fna29" class="fn" href="#fn29" title="Russell 1994; Russell et al. 2003; Gendron et al. 2014; Feldman Barrett 2017.">[29]</a>. | ||
| 359 | </p> | ||
| 360 | <p id="pid20"><span style="float: right; padding: .2em; color: #999; border: 1px solid #ccc; background-color: #eee; margin-right: -80px;">[<a href="#pid20">20</a>]</span>Ekman and colleagues, however, defined clearly how basic emotions can be | ||
| 361 | distinguished from other emotions: There are distinctive universal | ||
| 362 | signals, the presence in other primates, distinctive phyiosology, | ||
| 363 | distinctive universals in antecedent events, coherence in the emotional | ||
| 364 | response, a quick onset, a brief duration, an automatic appraisal, and | ||
| 365 | an automatic, unbidden occurrence. The set | ||
| 366 | of emotions that is typically | ||
| 367 | referred to as "Ekman emotions" consists of anger, fear, joy, sadness, | ||
| 368 | surprise, and disgust. Given that this set of emotions is relevant for | ||
| 369 | many studies, and that these emotion categories do not deserve further | ||
| 370 | explanation to most people, it constitutes a popular basis for | ||
| 371 | computational analysis. | ||
| 372 | </p> | ||
| 373 | </div><a name="div9"> </a><div id="subchapter"><a name="hd7"> </a><h3> | ||
| 236 | <div style="position:relative;width:90%;">2.2 Plutchik’s Wheel of Emotions</div> | 374 | <div style="position:relative;width:90%;">2.2 Plutchik’s Wheel of Emotions</div> |
| 237 | </h3> | 375 | </h3> |
| 238 | <p>Another influential model of emotions was proposed by Robert Plutchik in the early | 376 | <p id="pid21"><span style="float: right; padding: .2em; color: #999; border: 1px solid #ccc; background-color: #eee; margin-right: -80px;">[<a href="#pid21">21</a>]</span>Another influential model of emotions was proposed by Robert Plutchik |
| 239 | 1980s.<a id="fna29" class="fn" href="#fn29" title="Plutchik 1991, passim.">[29]</a> The important difference | 377 | in the early 1980s.<a id="fna30" class="fn" href="#fn30" title="Plutchik 1991.">[30]</a> The |
| 240 | between Plutchik’s theory and Ekman’s theory is that apart from a small set of basic | 378 | important difference between Plutchik’s theory and Ekman’s theory is |
| 241 | emotions, all other emotions are mixed and derived from the various combinations of | 379 | that apart from a small set of basic emotions, all other emotions |
| 242 | basic ones. He further categorized these other emotions into the <span style="color:#035151"><i>primary dyads</i></span> (very likely to co-occur), <span style="color:#035151"><i>secondary | 380 | are mixed and derived from the various |
| 243 | dyads</i></span> (less likely to co-occur) and <span style="color:#035151"><i>tertiary dyads</i></span> | 381 | combinations of basic ones. |
| 244 | (seldom co-occur). | 382 | He further categorized these other emotions into the <span style="color:#035151"><i>primary dyads</i></span> (very likely to co-occur), <span style="color:#035151"><i>secondary dyads</i></span> (less likely to co-occur) and <span style="color:#035151"><i>tertiary dyads</i></span> (seldom co-occur). |
| 245 | </p> | 383 | </p> |
| 246 | <p>In order to represent the organization and properties of emotions as defined by his | 384 | <p id="pid22"><span style="float: right; padding: .2em; color: #999; border: 1px solid #ccc; background-color: #eee; margin-right: -80px;">[<a href="#pid22">22</a>]</span>In order to represent the organization and properties of emotions as |
| 247 | theory, Plutchik proposed a structural model of emotions known nowadays as <i>Plutchik’s wheel of emotions</i>. The wheel <a title="" href="#emotion_analysis_2019_001"><span class="medium">Figure 1</span></a> is constructed in the fashion of a color wheel, with | 385 | defined by his theory, Plutchik proposed a structural model of |
| 248 | similar emotions placed closer together and opposite emotions 180 degrees apart. The | 386 | emotions known nowadays as <i>Plutchik’s wheel of emotions</i>. The wheel (<a title="" href="#emotion_analysis_2019_001"><span class="medium">Figure 1</span></a>) is constructed in the fashion of a color |
| 249 | intensity of an emotion in the wheel depends on how far from the center a part of | 387 | wheel, with similar emotions placed closer together and opposite |
| 250 | a | 388 | emotions 180 degrees apart. The intensity of an emotion in the wheel |
| 251 | petal is, i.e., emotions become less distinguishable the further they are from the | 389 | depends on how far from the center a part of a petal is, i.e., |
| 252 | center of the wheel. Essentially, the wheel is constructed from eight basic bipolar | 390 | emotions become less distinguishable the further they are from the |
| 253 | emotions: <i>joy</i> versus <i>sorrow</i>, <i>anger</i> versus <i>fear</i>, <i>trust</i> versus <i>disgust</i>, and <i>surprise</i> versus <i>anticipation</i>. The blank spaces | 391 | center of the wheel. Essentially, the wheel is constructed from |
| 254 | between the leaves are so-called <span style="color:#035151"><i>primary dyads</i></span> – emotions that | 392 | eight basic bipolar emotions: <i>joy</i> versus <i>sadness</i>, <i>anger</i> versus |
| 255 | are mixtures of two of the primary emotions. | 393 | <i>fear</i>, <i>trust</i> versus |
| 256 | </p> | 394 | <i>disgust</i>, and <i>surprise</i> versus <i>anticipation</i>. The |
| 257 | <p>The <i>wheel model of emotions</i> proposed by Plutchik had a great impact on the field of affective computing | 395 | blank spaces between the leaves are so-called <span style="color:#035151"><i>primary dyads</i></span> – emotions that are mixtures of two of the |
| 258 | being primarily used as a basis for emotion categorization in emotion recognition | 396 | primary emotions. |
| 259 | from text.<a id="fna30" class="fn" href="#fn30" title="Cambria et al. 2012; Kim et al. 2012; Suttles / Ide 2013; Borth et al. 2013; Abdul-Mageed / Ungar 2017.">[30]</a> However, some postulates of the theory are criticized, | 397 | </p> |
| 260 | for example, there is no empirical support for the wheel structure.<a id="fna31" class="fn" href="#fn31" title="Smith / Schneider 2009, passim.">[31]</a> Another criticism is that | 398 | <p id="pid23"><span style="float: right; padding: .2em; color: #999; border: 1px solid #ccc; background-color: #eee; margin-right: -80px;">[<a href="#pid23">23</a>]</span>The <i>wheel model of emotions</i> proposed by Plutchik had a great impact on the field of |
| 261 | Plutchik’s model of emotion does not explain the mechanisms by which <i>love</i>, <i>hate</i>, <i>relief</i>, <i>pride</i>, and other everyday emotions emerge | 399 | affective computing being primarily used as a basis for emotion |
| 262 | from the <i>basic</i> emotions, nor does it provide reliable | 400 | categorization in emotion recognition from text.<a id="fna31" class="fn" href="#fn31" title="Cambria et al. 2012; Kim et al. 2012; Suttles / Ide 2013; Borth et al. 2013; Abdul-Mageed / Ungar 2017.">[31]</a> |
| 263 | measurements of these emotions.<a id="fna32" class="fn" href="#fn32" title="Richins 1997, p. 128.">[32]</a></p> | 401 | However, some postulates of the theory are criticized, for example, |
| 402 | there is no empirical support for the wheel structure.<a id="fna32" class="fn" href="#fn32" title="Smith / Schneider 2009.">[32]</a> Another | ||
| 403 | criticism is that Plutchik’s model of emotions does not explain the | ||
| 404 | mechanisms by which non-basic emotions emerge from the <i>basic</i> emotions, nor does it provide reliable | ||
| 405 | measurements of these emotions.<a id="fna33" class="fn" href="#fn33" title="Richins 1997, p. 128.">[33]</a></p> | ||
| 264 | <div class="medium"> | 406 | <div class="medium"> |
| 265 | <div class="field-item even" rel="og:image rdfs:seeAlso" resource="../medium1"><a href="http://www.zfdg.de/sites/default/files/medien/emotion_analysis_2019_001.png" title="Fig. 1: Plutchik’s wheel of emotions. [Plutchik 2011. PD]" rel="gallery-node" class="colorbox"><img style="max-height:450px!important" class="artikel" alt="Fig. 1: Plutchik’s wheel of emotions. [Plutchik 2011. | 407 | <div class="field-item even" rel="og:image rdfs:seeAlso" resource="../medium1"><a href="http://www.zfdg.de/sites/default/files/medien/emotion_analysis_2019_001.png" title="Fig. 1: Plutchik’s wheel of emotions. [Plutchik 2011. PD]" rel="gallery-node" class="colorbox"><img style="max-height:450px!important" class="artikel" alt="Fig. 1: Plutchik’s wheel of emotions. [Plutchik 2011.
 PD] " id="emotion_analysis_2019_001" src="http://www.zfdg.de/sites/default/files/styles/medium_in_artikel/emotion_analysis_2019_001.png"></a></div> |
| 266 | <div class="img_desc"><a href="#abb1">Fig. 1</a>: Plutchik’s wheel of emotions. [<a href="#plutchik_wheel_2011">Plutchik 2011</a>. | 408 | <div class="img_desc"><a href="#abb1">Fig. 1</a>: Plutchik’s wheel of emotions. [<a href="#plutchik_wheel_2011">Plutchik 2011</a>. |
| 267 | <a href="https://creativecommons.org/publicdomain/mark/1.0/deed.de">PD</a>] | 409 | <a href="https://creativecommons.org/publicdomain/mark/1.0/deed.de">PD</a>] <a href="#emotion_analysis_2019_001"></a></div> |
| 268 | <a href="#emotion_analysis_2019_001"></a></div> | 410 | </div> |
| 269 | </div> | 411 | </div><a name="div10"> </a><div id="subchapter"><a name="hd8"> </a><h3> |
| 270 | </div> | ||
| 271 | <div id="subchapter"><a name="hd6"> </a><h3> | ||
| 272 | <div style="position:relative;width:90%;">2.3 Russel’s Circumplex Model</div> | 412 | <div style="position:relative;width:90%;">2.3 Russel’s Circumplex Model </div> |
| 273 | </h3> | 413 | </h3> |
| 274 | <p>Attempts to overcome the shortcomings of basic emotions theory and its unfitness for | 414 | <p id="pid24"><span style="float: right; padding: .2em; color: #999; border: 1px solid #ccc; background-color: #eee; margin-right: -80px;">[<a href="#pid24">24</a>]</span>Attempts to overcome the shortcomings of basic emotion theories |
| 275 | clinical studies led researchers to suggest various dimensional models, the most | 415 | and its unfitness for clinical studies led researchers to |
| 276 | prominent of which is the circumplex model of affect proposed by James Russel.<a id="fna33" class="fn" href="#fn33" title="Russell 1980.">[33]</a> The word <span style="color:#035151"><i>circumplex</i></span> | 416 | suggest various dimensional models, the most prominent of which |
| 277 | in the name of the model refers to the fact that emotional episodes do not cluster | 417 | is the circumplex model of affect proposed by James Russel.<a id="fna34" class="fn" href="#fn34" title="Russell 1980.">[34]</a> The word <span style="color:#035151"><i>circumplex</i></span> in the name of the model refers |
| 278 | at | 418 | to the fact that emotional episodes do not cluster at the axes |
| 279 | the axes but rather at the periphery of a circle <a title="" href="#emotion_analysis_2019_002"><span class="medium">Figure 2</span></a>. At the core of the | 419 | but rather at the periphery of a circle (<a title="" href="#emotion_analysis_2019_002"><span class="medium">Figure 2</span></a>). At the core of |
| 280 | circumplex model is the notion of two dimensions plotted on a circle along horizontal | 420 | the circumplex model is the notion of two dimensions plotted on |
| 281 | and vertical axes. These dimensions are <i>valence</i> (how pleasant | 421 | a circle along horizontal and vertical axes. These dimensions |
| 282 | or unpleasant one feels) and <i>arousal</i> (the degree of calmness | 422 | are <i>valence</i> (how pleasant or unpleasant |
| 283 | or excitement). The number of dimensions is not strictly fixed and there are | 423 | one feels) and <i>arousal</i> (the degree of |
| 284 | adaptations of the model that incorporate more dimensions. One example of this is | 424 | calmness or excitement). The number of dimensions is not |
| 285 | the | 425 | strictly fixed and there are adaptations of the model that |
| 286 | <span style="color:#035151"><i>Valence-Arousal-Dominance model </i></span>that adds an additional | 426 | incorporate more dimensions. One example of this is the <span style="color:#035151"><i>Valence-Arousal-Dominance model </i></span>that adds |
| 287 | dimension of dominance, the degree of control one feels over the situation that | 427 | an additional dimension of dominance, the degree of control one |
| 288 | causes an emotion.<a id="fna34" class="fn" href="#fn34" title="Bradley / Lang 1994, p. 50.">[34]</a></p> | 428 | feels over the situation that causes an emotion.<a id="fna35" class="fn" href="#fn35" title="Bradley / Lang 1994, p. 50.">[35]</a></p> |
| 289 | <p>By moving from discrete categories to a dimensional representation, the researchers | 429 | <p id="pid25"><span style="float: right; padding: .2em; color: #999; border: 1px solid #ccc; background-color: #eee; margin-right: -80px;">[<a href="#pid25">25</a>]</span>By moving from discrete categories to a dimensional |
| 290 | are able to account for subjective experiences that do not fit nicely into the | 430 | representation, the researchers are able to account for |
| 291 | isolated non-overlapping categories. Accordingly, each affective experience can be | 431 | subjective experiences that do not fit nicely into the isolated |
| 292 | depicted as a point in a <i>circumplex</i> that is described by only | 432 | non-overlapping categories. Accordingly, each affective |
| 293 | two parameters – <i>valence</i> and <i>arousal</i> – | 433 | experience can be depicted as a point in a <i>circumplex</i> that is described by only two parameters – |
| 294 | without need for labeling or reference to emotion concepts for which a name might | 434 | <i>valence</i> and <i>arousal</i> – without need for labeling or reference to |
| 295 | only exist in particular subcommunities or which are difficult to describe.<a id="fna35" class="fn" href="#fn35" title="Russell 2003, p. 154.">[35]</a> However, the strengths of the model turned | 435 | emotion concepts for which a name might only exist in particular |
| 296 | out to be its weaknesses: for example, it is not clear whether there are basic | 436 | subcommunities or which are difficult to describe.<a id="fna36" class="fn" href="#fn36" title="Russell 2003, p. 154.">[36]</a> |
| 297 | dimensions in the model<a id="fna36" class="fn" href="#fn36" title="Larsen / Diener 1992, p. 25.">[36]</a> nor is it | 437 | However, the |
| 298 | clear what should be done with qualitatively different events of <i>fear</i>, <i>anger</i>, <i>embarrassment</i> and | 438 | strengths of the model turned out to be its weaknesses: for |
| 299 | <i>disgust</i> that fall in identical places in the circumplex | 439 | example, it is not clear whether there are basic dimensions in |
| 300 | structure.<a id="fna37" class="fn" href="#fn37" title="Russell / Feldman Barrett 1999, p. 807.">[37]</a> Despite these | 440 | the model<a id="fna37" class="fn" href="#fn37" title="Larsen / Diener 1992, p. 25.">[37]</a> nor is it clear what should be done with |
| 301 | shortcomings, the circumplex model of affect is widely used in psychologic and | 441 | qualitatively different events of <i>fear</i>, |
| 302 | psycholinguistic studies. In computational linguistics, the circumplex model is | 442 | <i>anger</i>, <i>embarrassment</i> and <i>disgust</i> that |
| 303 | applied when the interest is in continuous measurements of <i>valence</i> and <i>arousal</i> rather than in the specific | 443 | fall in identical places in the circumplex structure.<a id="fna38" class="fn" href="#fn38" title="Russell / Feldman Barrett 1999, p. 807.">[38]</a> |
| 444 | Despite these shortcomings, the circumplex model of affect is | ||
| 445 | popular in psychologic and psycholinguistic studies, because | ||
| 446 | both dimensions can reliably be measured.<a id="fna39" class="fn" href="#fn39" title="Mauss / Robinson 2009.">[39]</a> In computational linguistics, | ||
| 447 | the circumplex model is applied when the interest is in | ||
| 448 | continuous measurements of <i>valence</i> and | ||
| 449 | <i>arousal</i> rather than in the specific | ||
| 304 | discrete emotional categories. | 450 | discrete emotional categories. |
| 305 | </p> | 451 | </p> |
| 452 | <p id="pid26"><span style="float: right; padding: .2em; color: #999; border: 1px solid #ccc; background-color: #eee; margin-right: -80px;">[<a href="#pid26">26</a>]</span>There are other models which locate discrete emotion categories | ||
| 453 | in a dimensional space, however, these have not been used in | ||
| 454 | computational literary studies yet (though such approaches are | ||
| 455 | promising also in this domain and constitute promising future | ||
| 456 | research). One instance, next to valence/arousal, are appraisal | ||
| 457 | theories<a id="fna40" class="fn" href="#fn40" title="Scherer 2005.">[40]</a> which | ||
| 458 | state that different dimensions, which measure how a stimulus | ||
| 459 | event is cognitively evaluated enable different sets of | ||
| 460 | emotions. The work by Smith and Ellsworth<a id="fna41" class="fn" href="#fn41" title="Smith / Ellsworth 1985.">[41]</a> shows that the six dimensions | ||
| 461 | of (1) how pleasant an event is, (2) how much effort an event | ||
| 462 | can be expected to cause, (3) how certain the experiencer is in | ||
| 463 | a specific situation, (4) how much attention is devoted to the | ||
| 464 | event, (5) how much responsibility the experiencer of the | ||
| 465 | emotion holds for what has happened, and (6) how much the | ||
| 466 | experiencer has control over the situation, explain 15 discrete | ||
| 467 | emotions. | ||
| 468 | </p> | ||
| 306 | <div class="medium"> | 469 | <div class="medium"> |
| 307 | <div class="field-item even" rel="og:image rdfs:seeAlso" resource="../medium1"><a href="http://www.zfdg.de/sites/default/files/medien/emotion_analysis_2019_002.png" title="Fig. 2: Circumplex model of affect: Horizontal axis represents the valence dimension, the vertical axis represents the arousal dimension. Drawn after Posner et al. 2005. [Kim / Klinger 2019]" rel="gallery-node" class="colorbox"><img style="max-height:450px!important" class="artikel" alt="Fig. 2: Circumplex model of affect: Horizontal axis represents the valence dimension, 
 the vertical axis represents the arousal dimension. Drawn after Posner et al. 2005. [Kim / Klinger 2019]" id="emotion_analysis_2019_002" src="http://www.zfdg.de/sites/default/files/styles/medium_in_artikel/emotion_analysis_2019_002.png"></a></div> | 470 | <div class="field-item even" rel="og:image rdfs:seeAlso" resource="../medium1"><a href="http://www.zfdg.de/sites/default/files/medien/emotion_analysis_2019_002.png" title="Fig. 2: Circumplex model of affect: Horizontal axis represents the valence dimension, the vertical axis represents the arousal dimension. Drawn after Posner et al. 2005. [Kim / Klinger 2019]" rel="gallery-node" class="colorbox"><img style="max-height:450px!important" class="artikel" alt="Fig. 2: Circumplex model of affect: Horizontal
 axis represents the valence dimension, the vertical axis represents the
 arousal dimension. Drawn after Posner et al. 2005. [Kim / Klinger
 2019]" id="emotion_analysis_2019_002" src="http://www.zfdg.de/sites/default/files/styles/medium_in_artikel/emotion_analysis_2019_002.png"></a></div> |
| 308 | <div class="img_desc"><a href="#abb2">Fig. 2</a>: Circumplex model of affect: Horizontal axis represents the valence dimension, | 471 | <div class="img_desc"><a href="#abb2">Fig. 2</a>: Circumplex model of affect: Horizontal |
| 309 | the vertical axis represents the arousal dimension. Drawn after <a href="#posner_model_2005">Posner et al. 2005</a>. [Kim / Klinger 2019]<a href="#emotion_analysis_2019_002"></a></div> | 472 | axis represents the valence dimension, the vertical axis represents the |
| 310 | </div> | 473 | arousal dimension. Drawn after <a href="#posner_model_2005">Posner et al. 2005</a>. [Kim / Klinger |
| 311 | </div> | 474 | 2019]<a href="#emotion_analysis_2019_002"></a></div> |
| 312 | </div> | 475 | </div> |
| 313 | <div id="chapter"><a name="hd7"> </a><h2> | 476 | </div> |
| 477 | </div><a name="div11"> </a><div id="chapter"><a name="hd9"> </a><h2> | ||
| 314 | <div style="position:relative;width:90%;">3 Emotion Analysis in Non-computational Literary Studies</div> | 478 | <div style="position:relative;width:90%;">3 Emotion Analysis in Non-computational Literary Studies</div> |
| 315 | </h2> | 479 | </h2> |
| 316 | <p>Until the end of the twentieth century, literary and art theories often disregarded | 480 | <p id="pid27"><span style="float: right; padding: .2em; color: #999; border: 1px solid #ccc; background-color: #eee; margin-right: -80px;">[<a href="#pid27">27</a>]</span>In the past, literary and art theories often disregarded the importance of |
| 317 | the importance of the aesthetic and affective dimension of literature, which in part | 481 | the aesthetic and affective dimension of literature, which in part stemmed |
| 318 | stemmed from the rejection of old-fashioned literary history that had explained the | 482 | from the rejection of old-fashioned literary history that had explained the |
| 319 | meaning of art works by the biography of the author.<a id="fna38" class="fn" href="#fn38" title="Sætre et al. 2014b, passim.">[38]</a> However, the affective turn taken by a wide range of | 483 | meaning of art works by the biography of the author.<a id="fna42" class="fn" href="#fn42" title="Sætre et al. 2014b.">[42]</a> However, the affective turn taken by a wide |
| 320 | disciplines in the past two decades – from political and sociological sciences to | 484 | range of disciplines in the past two decades – from political and |
| 321 | neurosciences or media studies – has refueled the interest of literary critics in | 485 | sociological sciences to neurosciences or media studies – has refueled the |
| 322 | human affects and sentiments. | 486 | interest of literary critics in human affects and sentiments. |
| 323 | </p> | 487 | </p> |
| 324 | <p>We said in <a title="" href="#hd1">Section 1</a> that there seems to be a consensus among literary critics that | 488 | <p id="pid28"><span style="float: right; padding: .2em; color: #999; border: 1px solid #ccc; background-color: #eee; margin-right: -80px;">[<a href="#pid28">28</a>]</span>We said in <a title="" href="#hd1">section 1</a> that there seems |
| 325 | literary art and emotions go hand in hand. However, one might be challenged to define | 489 | to be a consensus among literary critics that literary art and emotions go |
| 326 | the specific way in which emotions come into play in the text. The exploration of | 490 | hand in hand. However, one might be challenged to define the specific way in |
| 327 | this problem is presented by van Meel.<a id="fna39" class="fn" href="#fn39" title="Van Meel 1995, passim.">[39]</a> | 491 | which emotions come into play in the text. The exploration of this problem |
| 328 | Underpinning the centrality of human destiny, hopes, and feelings in the themes of | 492 | is presented by van Meel.<a id="fna43" class="fn" href="#fn43" title="Van Meel 1995.">[43]</a> |
| 329 | many artworks – from painting to literature – van Meel explores how emotions are | 493 | Underpinning the centrality of human destiny, hopes, and feelings in the |
| 330 | involved in the production of arts. Pointing out big differences between the two | 494 | themes of many artworks – from painting to literature – van Meel explores |
| 331 | media in their attempts to depict human emotions (painting conveys nonverbal behavior | 495 | how emotions are involved in the production of arts. Pointing out big |
| 332 | directly, but lacks temporal dimensions that novels have and use to describe | 496 | differences between the two media in their attempts to depict human emotions |
| 333 | emotions), van Meel provides an analysis of the nonverbal descriptions used by the | 497 | (painting conveys nonverbal behavior directly, but lacks temporal dimensions |
| 334 | writers to convey their characters’ emotional behavior. Description of visual | 498 | that novels have and use to describe emotions), van Meel provides an |
| 335 | characteristics, van Meel speculates, responds to a fundamental need of a reader to | 499 | analysis of the nonverbal descriptions used by the writers to convey their |
| 336 | build an image of a person and their behavior. Moreover, nonverbal descriptions add | 500 | characters’ emotional behavior. Description of visual characteristics, van |
| 337 | important information that can in some cases play a crucial hermeneutical role, such | 501 | Meel speculates, responds to a fundamental need of a reader to build an |
| 338 | as in Kafka’s <i>Der Prozess</i>, where the fatal decisions for K. are made clear by gestures rather than | 502 | image of a person and their behavior. Moreover, nonverbal descriptions add |
| 339 | words. His verdict is not announced, but is implied by the judge who refuses a | 503 | important information that can in some cases play a crucial hermeneutical |
| 340 | handshake. The same applies to his death sentence that is conveyed to him by his | 504 | role, such as in Kafka’s <i>Der Prozess</i>, where the fatal decisions for K. are made clear by gestures rather |
| 341 | executioners playing with a butcher’s knife above his head. | 505 | than words. His verdict is not announced, but is implied by the judge who |
| 342 | </p> | 506 | refuses a handshake. The same applies to his death sentence that is conveyed |
| 343 | <p>A hermeneutic approach through the lense of emotions is presented by Kuivalainen<a id="fna40" class="fn" href="#fn40" title="Kuivalainen 2009, passim.">[40]</a> and provides a detailed analysis of | 507 | to him by his executioners playing with a butcher’s knife above his head. |
| 344 | linguistic features that contribute to the characters’ emotional involvement in | 508 | These aspects how emotions are communicated clearly point to challenges for |
| 345 | Mansfield’s prose. The study shows how, through the extensive use of adjectives, | 509 | computational methods – implicit descriptions, world knowledge, and |
| 346 | adverbs, deictic markers, and orthography, Mansfield steers the reader towards the | 510 | inference steps that are grounded in combinations of text and readers' |
| 347 | protagonist’s climax. Subtly shifting between psycho-narration and free indirect | 511 | experiences have not been tackled with computational methods yet. |
| 348 | discourse, Mansfield is making use of evaluative and emotive descriptors in | 512 | </p> |
| 349 | psycho-narrative sections, often marking the internal discourse with dashes, | 513 | <p id="pid29"><span style="float: right; padding: .2em; color: #999; border: 1px solid #ccc; background-color: #eee; margin-right: -80px;">[<a href="#pid29">29</a>]</span>A hermeneutic approach through the lense of emotions is presented by |
| 350 | exclamation marks, intensifiers, and repetition that thus trigger an emotional | 514 | Kuivalainen<a id="fna44" class="fn" href="#fn44" title="Kuivalainen 2009.">[44]</a> and provides |
| 351 | climax. Various deictic features introduced in the text are used to pinpoint the | 515 | a detailed analysis of linguistic features that contribute to the |
| 352 | source of emotions, which helps in creating a picture of characters’ emotional world. | 516 | characters’ emotional involvement in Katherine Mansfield’s prose. The study |
| 353 | Verbs (especially in the present tense), adjectives, and adverbs serve the same goal | 517 | shows how, through the extensive use of adjectives, adverbs, deictic |
| 354 | in Mansfield’s prose of describing the characters’ emotional world. Going back and | 518 | markers, and orthography, Mansfield steers the reader towards the |
| 355 | forth from psycho-narration to free indirect discourse provides Mansfield with a tool | 519 | protagonist’s climax. Subtly shifting between psycho-narration and free |
| 356 | to point out the significant moments in the protagonists’ lives and establish a | 520 | indirect discourse, Mansfield is making use of evaluative and emotive |
| 357 | separation between characters and narration. | 521 | descriptors in psycho-narrative sections, often marking the internal |
| 358 | </p> | 522 | discourse with dashes, exclamation marks, intensifiers, and repetition that |
| 359 | <p>Both van Meel’s and Kuivalainen’s works, separated from each other by more than a | 523 | thus trigger an emotional climax. Various deictic features introduced in the |
| 360 | decade, underpin the importance of emotions in the interpretation of characters’ | 524 | text are used to pinpoint the source of emotions, which helps in creating a |
| 361 | traits, hopes, and tragedy. Other authors find these connections as well. For | 525 | picture of characters’ emotional world. Verbs (especially in the present |
| 362 | example, Barton<a id="fna41" class="fn" href="#fn41" title="Barton 1996, passim.">[41]</a> proposes instructional | 526 | tense), adjectives, and adverbs serve the same goal in Mansfield’s prose of |
| 363 | approaches to teach school-level readers to interpret character’s emotions and use | 527 | describing the characters’ emotional world. Going back and forth from |
| 364 | this information for story interpretation. Van Horn<a id="fna42" class="fn" href="#fn42" title="Van Horn 1997, passim.">[42]</a> shows that understanding characters emotionally or trying to help | 528 | psycho-narration to free indirect discourse provides Mansfield with a tool |
| 365 | them with their problems made reading and writing more meaningful for middle school | 529 | to point out the significant moments in the protagonists’ lives and |
| 530 | establish a separation between characters and narration. This study | ||
| 531 | illustrates another challenge for automatic methods. Computational models | ||
| 532 | mostly rely on isolated, comparable short, units of the text. The broader | ||
| 533 | context, let alone the development of characters, are mostly ignored in | ||
| 534 | computational analysis – a prediction depends on the local description and | ||
| 535 | is not conditioned on previous experiences. That is a clear disadvantage of | ||
| 536 | distant reading methods to close reading. | ||
| 537 | </p> | ||
| 538 | <p id="pid30"><span style="float: right; padding: .2em; color: #999; border: 1px solid #ccc; background-color: #eee; margin-right: -80px;">[<a href="#pid30">30</a>]</span>Both van Meel’s and Kuivalainen’s works, separated from each other by more | ||
| 539 | than a decade, underpin the importance of emotions in the interpretation of | ||
| 540 | characters’ traits, hopes, and tragedy. Other authors find these connections | ||
| 541 | as well. For example, Barton<a id="fna45" class="fn" href="#fn45" title="Barton 1996.">[45]</a> | ||
| 542 | proposes instructional approaches to teach school-level readers to interpret | ||
| 543 | character’s emotions and use this information for story interpretation. Van | ||
| 544 | Horn<a id="fna46" class="fn" href="#fn46" title="Van Horn 1997.">[46]</a> shows that | ||
| 545 | understanding characters emotionally or trying to help them with their | ||
| 546 | problems made reading and writing more meaningful for middle school | ||
| 366 | students. | 547 | students. |
| 367 | </p> | 548 | </p> |
| 368 | <p>Emotions in text are often conveyed with emotion-bearing words.<a id="fna43" class="fn" href="#fn43" title="Johnson-Laird / Oatley 1989, passim.">[43]</a> At the same time their role in the creation | 549 | <p id="pid31"><span style="float: right; padding: .2em; color: #999; border: 1px solid #ccc; background-color: #eee; margin-right: -80px;">[<a href="#pid31">31</a>]</span>Emotions in text are often conveyed with emotion-bearing words.<a id="fna47" class="fn" href="#fn47" title="Johnson-Laird / Oatley 1989.">[47]</a> At the same time |
| 369 | and depiction of emotion should not be overestimated. That is, saying that someone | 550 | their role in the creation and depiction of emotion should not be |
| 370 | looked angry or fearful or sad, as well as directly expressing characters’ emotions, | 551 | overestimated. That is, saying that someone looked angry or fearful or sad, |
| 371 | are not the only ways authors build believable fictional spaces filled with | 552 | as well as directly expressing characters’ emotions, are not the only ways |
| 372 | characters, action, and emotions. In fact, many novelists strive to express emotions | 553 | authors build believable fictional spaces filled with characters, action, |
| 373 | indirectly by way of figures of speech or catachresis,<a id="fna44" class="fn" href="#fn44" title="Miller 2014, p. 92.">[44]</a> first of all because emotional language can be | 554 | and emotions. In fact, many novelists strive to express emotions indirectly |
| 374 | ambiguous and vague, and, second, to avoid any allusions to Victorian emotionalism | 555 | by way of figures of speech or catachresis,<a id="fna48" class="fn" href="#fn48" title="Miller 2014, p. 92.">[48]</a> first of all because emotional language can be |
| 375 | and pathos. | 556 | ambiguous and vague, and, second, to avoid any allusions to Victorian |
| 376 | </p> | 557 | emotionalism and pathos. |
| 377 | <p>How can an author convey emotions indirectly? A book chapter by Hillis Miller in <i>Exploring Text and Emotions</i><a id="fna45" class="fn" href="#fn45" title="Sætre et al. 2014a, p. 91ff.">[45]</a> seeks the answer to exactly this | 558 | </p> |
| 378 | question. Using Conrad’s <i>Nostromo</i> opening scenes as material, Hillis Miller shows how Conrad’s descriptions of | 559 | <p id="pid32"><span style="float: right; padding: .2em; color: #999; border: 1px solid #ccc; background-color: #eee; margin-right: -80px;">[<a href="#pid32">32</a>]</span>How can an author convey emotions indirectly? A book chapter by Hillis Miller |
| 379 | an imaginary space generate emotions in readers without direct communication of | 560 | in <i>Exploring Text and Emotions</i><a id="fna49" class="fn" href="#fn49" title="Sætre et al. 2014a, pp. 91ff.">[49]</a> seeks the answer |
| 380 | emotions. Conrad’s <i>Nostromo</i> opening chapter is an objective description of Sulaco, an imaginary land. The | 561 | to exactly this question. Using Joseph Conrad’s <i>Nostromo</i> opening scenes as material, Miller shows how Conrad’s descriptions |
| 381 | description is mainly topographical and includes occasional architectural metaphors, | 562 | of an imaginary space generate emotions in readers without direct |
| 382 | but it combines wide expanse with hermetically sealed enclosure, which generates | 563 | communication of emotions. Conrad’s <i>Nostromo</i> opening chapter is an objective description of Sulaco, an imaginary |
| 383 | depthless emotional detachment<a id="fna46" class="fn" href="#fn46" title="Miller 2014, p. 93.">[46]</a>. Through the use of present tense, Conrad makes the readers suggest | 564 | land. The description is mainly topographical and includes occasional |
| 384 | that the whole scene is timeless and does not change. The topographical descriptions | 565 | architectural metaphors, but it combines wide expanse with hermetically |
| 385 | are given in a pure materialist way: there is nothing behind clouds, mountains, | 566 | sealed enclosure, which generates »depthless emotional |
| 386 | rocks, and sea that would matter to humankind, not a single feature of the landscape | 567 | detachment«<a id="fna50" class="fn" href="#fn50" title="Miller 2014, p. 93.">[50]</a>. Through the use of |
| 387 | is personified, and not a single topographical shape is symbolic. Knowingly or | 568 | present tense, Conrad makes the readers suggest that the whole scene is |
| 388 | unknowingly, Miller argues, by telling the readers what they should see – with no | 569 | timeless and does not change. The topographical descriptions are given in a |
| 389 | deviations from truth – Conrad employs a trope that perfectly matches Kant’s <i>concept of the sublime</i>. Kant’s view of poetry was that true poets tell the truth without | 570 | pure materialist way: there is nothing behind clouds, mountains, rocks, and |
| 390 | interpretation; they do not deviate from what their eyes see. Conrad, or to be more | 571 | sea that would matter to humankind, not a single feature of the landscape is |
| 391 | specific, his narrator in <i>Nostromo</i>, is an example of sublime seeing with a latent presence of strong emotions. | 572 | personified, and not a single topographical shape is symbolic. Knowingly or |
| 392 | On the one hand, Conrad’s descriptions are cool and detached. This coolness is caused | 573 | unknowingly, Miller argues, by telling the readers what they should see – |
| 393 | by the indifference of the elements in the scene. On the other hand, by dehumanizing | 574 | with no deviations from truth – Conrad employs a trope that perfectly |
| 394 | sea and sky, Conrad generates awe, fear, and a dark foreboding about the kinds of | 575 | matches Immanuel Kant’s <i>concept of the sublime</i>. Kant’s view of poetry was that true poets tell the truth without |
| 395 | life stories that are likely to be enacted against such a backdrop<a id="fna47" class="fn" href="#fn47" title="Miller 2014, p. 115.">[47]</a>. | 576 | interpretation; they do not deviate from what their eyes see. Conrad, or to |
| 396 | </p> | 577 | be more specific, his narrator in <i>Nostromo</i>, is an example of sublime seeing with a latent presence of strong |
| 397 | <p>Hillis Miller’s analysis resonates with some premises from emotion theory that we | 578 | emotions. On the one hand, Conrad’s descriptions are cool and detached. This |
| 398 | have discussed previously, namely, Plutchik’s belief that emotions should be studied | 579 | coolness is caused by the indifference of the elements in the scene. On the |
| 399 | not by a certain way of expression but by the overall behavior of a person. | 580 | other hand, by dehumanizing sea and sky, Conrad generates »awe, fear, |
| 400 | Considering that such a formula cannot be applied to all literary theory studies | 581 | and a dark foreboding about the kinds of life stories that are likely to |
| 401 | about emotions (as not all authors choose to convey emotions indirectly, as well as | 582 | be enacted against such a backdrop.«<a id="fna51" class="fn" href="#fn51" title="Miller 2014, p. 115.">[51]</a></p> |
| 402 | not all authors tend to comment on characters’ nonverbal emotional behavior), it | 583 | <p id="pid33"><span style="float: right; padding: .2em; color: #999; border: 1px solid #ccc; background-color: #eee; margin-right: -80px;">[<a href="#pid33">33</a>]</span>Hillis Miller’s analysis resonates with some premises from emotion theory |
| 403 | seems that one should search for a balance between low-level linguistic feature | 584 | that we have discussed previously, namely, Plutchik’s belief that emotions |
| 404 | analysis of emotional language and a rigorous high-level hermeneutic inquiry | 585 | should be studied not by a certain way of expression but by the overall |
| 405 | dissecting the form of the novel and its under-covered philosophical layers. | 586 | behavior of a person. Considering that such a formula cannot be applied to |
| 406 | </p> | 587 | all literary theory studies about emotions (as not all authors choose to |
| 407 | </div> | 588 | convey emotions indirectly, as well as not all authors tend to comment on |
| 408 | <div id="chapter"><a name="hd8"> </a><h2> | 589 | characters’ nonverbal emotional behavior), it seems that one should search |
| 590 | for a balance between low-level linguistic feature analysis of emotional | ||
| 591 | language and a rigorous high-level hermeneutic inquiry dissecting the form | ||
| 592 | of the novel and its under-covered philosophical layers.<a id="fna52" class="fn" href="#fn52" title="We recommend the essay by Katja Mellmann for further details on that topic. Mellmann 2002.">[52]</a></p> | ||
| 593 | </div><a name="div12"> </a><div id="chapter"><a name="hd10"> </a><h2> | ||
| 409 | <div style="position:relative;width:90%;">4 Emotion and Sentiment Analysis in Computational Literary Studies</div> | 594 | <div style="position:relative;width:90%;">4 Emotion and Sentiment Analysis in Computational Literary Studies</div> |
| 410 | </h2> | 595 | </h2> |
| 411 | <p>With this section, we proceed to an overview of the existing body of research | 596 | <p id="pid34"><span style="float: right; padding: .2em; color: #999; border: 1px solid #ccc; background-color: #eee; margin-right: -80px;">[<a href="#pid34">34</a>]</span>With this section, we proceed to an overview of the existing body of research |
| 412 | computational analysis of emotion and sentiment in computational literary | 597 | on computational analysis of emotion and sentiment in computational literary |
| 413 | An | 598 | studies. An overview of the papers including their properties is shown in |
| 414 | | 599 | <a title="" href="#emotion_analysis_2019_003"><span class="medium">Table 1</span></a>. The table, |
| 415 | | 600 | as well as this section, is divided into several subsections, each of which |
| 416 | | 601 | corresponds to a specific application of emotion analysis to literature. |
| 417 | | 602 | <a title="" href="#hd11">section 4.1</a> reviews the papers |
| 418 | | 603 | that deal with the classification of literary texts in terms of emotions |
| 419 | | 604 | they convey; <a title="" href="#hd14">section 4.2</a> examines the |
| 420 | classification by genre or other story-types based | 605 | papers that address text classification by genre or other story-types based |
| 421 | <a title="" href=" | 606 | on sentiment and emotion features; <a title="" href="#hd17">section |
| 422 | | 607 | 4.3</a> is dedicated to research in modeling sentiments and emotions |
| 423 | | 608 | in texts from previous centuries, as well as research dealing with |
| 424 | analysis | 609 | applications of sentiment analysis to texts written in the past; <a title="" href="#hd21">section 4.4</a> provides an overview of |
| 425 | | 610 | sentiment analysis applications to character analysis and character network |
| 426 | | 611 | construction, and <a title="" href="#hd24">section 4.5 </a>is |
| 427 | | 612 | dedicated to more general applications. |
| 428 | <div id="subchapter"><a name=" | 613 | </p><a name="div13"> </a><div id="subchapter"><a name="hd11"> </a><h3> |
| 429 | <div style="position:relative;width:90%;">4.1 Emotion Classification</div> | 614 | <div style="position:relative;width:90%;">4.1 Emotion Classification</div> |
| 430 | </h3> | 615 | </h3> |
| 431 | <p>A straightforward approach to sentiment and emotion analysis is phrasing them as a | 616 | <p id="pid35"><span style="float: right; padding: .2em; color: #999; border: 1px solid #ccc; background-color: #eee; margin-right: -80px;">[<a href="#pid35">35</a>]</span>A straightforward approach to emotion analysis is text |
| 432 | text classification<a id="fna48" class="fn" href="#fn48" title="Liu 2015, p. 47.">[48]</a>. A fundamental | 617 | classification<a id="fna53" class="fn" href="#fn53" title="Liu 2015, p. 47.">[53]</a>. |
| 433 | question of such a classification is how to find the best features and algorithms | 618 | Indeed, emotion classification is one of the most popular subtasks and |
| 434 | to | 619 | finds application in several downstream tasks. A fundamental question of |
| 435 | classify the data (sentences, paragraphs, entire documents) into predefined classes. | 620 | such a classification is how to find the best input representations and |
| 436 | When applied to literature, such a classification may be of use for grouping | 621 | algorithms to classify the data (sentences, paragraphs, entire |
| 437 | different literary texts in digital collections based on the emotional properties | 622 | documents) into predefined classes. When applied to literature, such a |
| 438 | of | 623 | classification may be of use for grouping different literary texts in |
| 439 | the stories. For example, books or poems can be grouped based on the emotions they | 624 | digital collections based on the emotional properties of the stories or |
| 440 | convey or based on whether or not they have happy endings or not. | 625 | to perform other analyses regarding the distribution of emotions in |
| 441 | </p> | 626 | subcollections. For example, books or poems can be grouped based on the |
| 442 | <div id="subchapter"><a name="hd10"> </a><h3> | 627 | emotions they convey or based on whether or not they have happy endings |
| 628 | or not. | ||
| 629 | </p><a name="div14"> </a><div id="subchapter"><a name="hd12"> </a><h3> | ||
| 443 | <div style="position:relative;width:90%;">4.1.1 Classification based on emotions</div> | 630 | <div style="position:relative;width:90%;">4.1.1 Classification based on emotions</div> |
| 444 | </h3> | 631 | </h3> |
| 445 | <p>Barros et al.<a id="fna49" class="fn" href="#fn49" title="Barros et al. 2013, passim.">[49]</a> aim at answering two | 632 | <p id="pid36"><span style="float: right; padding: .2em; color: #999; border: 1px solid #ccc; background-color: #eee; margin-right: -80px;">[<a href="#pid36">36</a>]</span>Barros et al.<a id="fna54" class="fn" href="#fn54" title="Barros et al. 2013.">[54]</a> aim at |
| 446 | research questions: 1) is the classification of Quevedo’s works proposed by the | 633 | answering two research questions: 1) is the classification of |
| 447 | literary scholars consistent with the sentiment reflected by the corresponding | 634 | Francisco de Quevedo’s works proposed by the literary scholars |
| 448 | poems?; and 2) which learning algorithms are the best for the classification? To that | 635 | consistent with the sentiment reflected by the corresponding poems; |
| 449 | end, they perform a set of experiments on the classification of 185 Francisco de | 636 | and 2) which learning algorithms are the best for the classification |
| 450 | Quevedo’s poems that are divided by literary scholars into four categories and that | 637 | (the latter being an engineering question that is inherent in many |
| 451 | Barros et al. map to emotions of <i>joy</i>, <i>anger</i>, <i>fear</i>, and <i>sadness</i>. | 638 | of the papers that we discuss)? They perform a set of experiments on |
| 452 | Using the terms <i>joy</i>, <i>anger</i>, <i>fear</i>, and <i>sadness</i> as points of | 639 | the classification of 185 Francisco de Quevedo’s poems that are |
| 453 | reference, Barros et al. construct a list of emotion words by looking up the synonyms | 640 | divided by literary scholars into four categories and that Barros et |
| 454 | of English emotion words and adjectives associated with these four emotions and | 641 | al. map to emotions. Using the terms <i>joy</i>, <i>anger</i>, <i>fear</i>, and <i>sadness</i> as points of reference, Barros et al. |
| 455 | translating them into Spanish. Each poem is converted into a vector where each item | 642 | construct a list of emotion words by looking up the synonyms of |
| 456 | is a normalized count of words relating to a certain emotion. The experiments with | 643 | English emotion words and adjectives associated with these four |
| 457 | different algorithms show the superiority of decision trees achieving accuracy of | 644 | emotions and translating them into Spanish. This leads to a novel |
| 458 | almost 60%. However, this result is biased by an unbalanced distribution of classes. | 645 | and task-specific lexicon, to which each poem is then compared, |
| 459 | To avoid the bias, Barros et al. apply a resampling strategy that leads to a more | 646 | based on normalized term counts. The experiments show the |
| 460 | balanced distribution and repeat the classification experiments. After resampling, | 647 | superiority of decision trees as classification approach which can |
| 461 | the accuracy of decision trees in a 10-fold cross validation achieves 75,13%, thus | 648 | further be improved by rebalancing the collection via resampling. |
| 462 | demonstrating an improvement over the previous classification performance. Based on | 649 | Based on these results the authors conclude that a meaningful |
| 463 | these results the authors conclude that a meaningful classification of the literary | 650 | classification of the literary pieces based only on the emotion |
| 464 | pieces based only on the emotion information is possible. | 651 | information is possible. |
| 465 | </p> | 652 | </p> |
| 466 | <p>Reed<a id="fna50" class="fn" href="#fn50" title="Reed 2018, passim.">[50]</a> offers a <span style="color:#035151"><i>proof-of-concept</i></span> for performing sentiment analysis on a corpus of | 653 | <p id="pid37"><span style="float: right; padding: .2em; color: #999; border: 1px solid #ccc; background-color: #eee; margin-right: -80px;">[<a href="#pid37">37</a>]</span>A more modern corpus selection of poetry is the object of analysis by |
| 467 | twentieth-century American poetry. Specifically, Reed analyzes the expression of | 654 | Ethan Reed.<a id="fna55" class="fn" href="#fn55" title="Reed 2018">[55]</a>. The author |
| 468 | emotions in the poetry of the <span style="color:#035151"><i>Black Arts Movement</i></span> of the 1960s | 655 | offers a <span style="color:#035151"><i>proof-of-concept</i></span> for performing |
| 469 | and 1970s. The paper describes the project Measured Unrest in the Poetry of the Black | 656 | sentiment analysis on twentieth-century American poetry with |
| 470 | Arts Movement whose goal is to understand 1) how the feelings associated with | 657 | dictionary-based black-box sentiment analysis systems that output |
| 471 | injustice are coded in terms of race and gender, and 2) what sentiment analysis can | 658 | the polarity of a text. Specifically, they analyze the expression of |
| 472 | show us about the relations between affect and gender in poetry. Reed notes that | 659 | emotions in the poetry of the <span style="color:#035151"><i>Black Arts |
| 473 | surface affective value of the words does not always align with their more nuanced | 660 | Movement</i></span> of the 1960s and 1970s. The goal of the project |
| 474 | affective meaning shaped by poetic, social, and political contexts. | 661 | is to understand how feelings associated with injustice are coded in |
| 475 | </p> | 662 | terms of race and gender, and what sentiment analysis can show us |
| 476 | <p>Yu<a id="fna51" class="fn" href="#fn51" title="Yu 2008, passim.">[51]</a> explores what linguistic patterns | 663 | about the relations between affect and gender in poetry. Reed notes |
| 477 | characterize the genre of sentimentalism in early American novels. To that end, they | 664 | that the surface affective value of the words does not always align |
| 478 | construct a collection of five novels from the mid-nineteenth century and annotate | 665 | with their more nuanced affective meaning shaped by poetic, social, |
| 479 | the emotionality of each of the chapters as <i>high</i> or <i>low</i>. The respective chapters are then classified using | 666 | and political contexts. Therefore, this study can be seen as a |
| 480 | support-vector machines and naïve Bayes classifiers as highly emotional or the | 667 | critical reflection on methodological choices. |
| 481 | opposite. The results of the evaluation suggest that arbitrary feature reduction | 668 | </p> |
| 482 | steps such as stemming and stopword removal should be taken very carefully, as they | 669 | <p id="pid38"><span style="float: right; padding: .2em; color: #999; border: 1px solid #ccc; background-color: #eee; margin-right: -80px;">[<a href="#pid38">38</a>]</span>Yu<a id="fna56" class="fn" href="#fn56" title="Yu 2008.">[56]</a> explores linguistic patterns |
| 483 | may affect the prediction. For example, Yu shows that no stemming leads to better | 670 | that characterize the genre of sentimentalism in early American |
| 484 | classification results. A possible explanation is that stemming conflates and | 671 | novels. They analyze five novels from the mid-nineteenth century and |
| 485 | neutralizes a large number of discriminative features. The author provides an example | 672 | annotate the emotionality of each of the chapters as <i>high</i> or <i>low (not: positive |
| 486 | of such a conflation with the words <i>wilderness</i> and <i>wild</i>. While the latter can appear anywhere in the text, the | 673 | or negative!)</i>. This approach is noteworthy, as the unit of |
| 487 | former one is primarily encountered in the chapters filled with emotions. | 674 | analysis is comparably large in contrast to most sentiment analysis |
| 488 | </p> | 675 | methods. Each chapter is classified with standard configurations of |
| 489 | </div> | 676 | support vector machines and naïve Bayes classifiers, as highly |
| 490 | <div id="subchapter"><a name="hd11"> </a><h3> | 677 | emotional or the opposite. The results of the evaluation suggest |
| 491 | <div style="position:relative;width:90%;">4.1.2 Classification of happy ending vs. non-happy endings</div> | 678 | that arbitrary feature reduction steps such as stemming and stopword |
| 679 | removal should be taken very carefully, as they may affect the | ||
| 680 | prediction. | ||
| 681 | </p> | ||
| 682 | <p id="pid39"><span style="float: right; padding: .2em; color: #999; border: 1px solid #ccc; background-color: #eee; margin-right: -80px;">[<a href="#pid39">39</a>]</span>Volkova<a id="fna57" class="fn" href="#fn57" title="Volkova et al. 2010.">[57]</a> did not | ||
| 683 | focus on the classification of emotions automatically, but tackles | ||
| 684 | the task of annotation in more detail. The authors observe that | ||
| 685 | annotation of literature, in their case fairy tales, is challenging, | ||
| 686 | and that it is hard to obtain an acceptable annotation agreement. An | ||
| 687 | interesting innovative element in this study is that annotators were | ||
| 688 | not presented a predefined unit to annotate – they were allowed to | ||
| 689 | decide by themselves which granularity is most reasonable. That is | ||
| 690 | different to the other studies mentioned before in this section. | ||
| 691 | Further, a main finding was that short instances lead to a lower | ||
| 692 | agreement. | ||
| 693 | </p> | ||
| 694 | <p id="pid40"><span style="float: right; padding: .2em; color: #999; border: 1px solid #ccc; background-color: #eee; margin-right: -80px;">[<a href="#pid40">40</a>]</span>Finally, an interesting study by Ashok et al.<a id="fna58" class="fn" href="#fn58" title="Ashok et al. 2013?.">[58]</a> did not classify | ||
| 695 | emotions regarding a variable motivated by literary studies. They | ||
| 696 | use sentiment polarity as one component to predict the success of a | ||
| 697 | book. While such studies (similarly the prediction of citation | ||
| 698 | counts, etc.) are often criticized, the authors present some | ||
| 699 | interesting, but also perhaps non-surprising findings, e.g. that | ||
| 700 | unsuccessful stories contain more discriminative words that have a | ||
| 701 | negative connotation. | ||
| 702 | </p> | ||
| 703 | </div><a name="div15"> </a><div id="subchapter"><a name="hd13"> </a><h3> | ||
| 704 | <div style="position:relative;width:90%;">4.1.2 Classification of happy ending vs. non-happy | ||
| 705 | endings | ||
| 706 | </div> | ||
| 492 | </h3> | 707 | </h3> |
| 493 | <p>Zehe et al.<a id="fna52" class="fn" href="#fn52" title="Zehe et al. 2016, passim.">[52]</a> argue that automatically | 708 | <p id="pid41"><span style="float: right; padding: .2em; color: #999; border: 1px solid #ccc; background-color: #eee; margin-right: -80px;">[<a href="#pid41">41</a>]</span>A particular use case of emotion classification is to look closer |
| 494 | recognizing a happy ending as a major plot element could help to better understand | 709 | at particular parts of a text. Zehe et al.<a id="fna59" class="fn" href="#fn59" title="Zehe et al. 2016.">[59]</a> argue that automatically |
| 495 | a | 710 | recognizing a happy ending as a major plot element could help to |
| 496 | plot structure as a whole. To show that this is possible, they classify 212 German | 711 | better understand a plot structure as a whole. To show that this |
| 497 | novels written between 1750 and 1920 as having happy or non-happy endings. A novel | 712 | is possible, they classify 212 German novels written between |
| 498 | is | 713 | 1750 and 1920 as having happy or non-happy endings. A novel is |
| 499 | considered to have a happy ending if the situation of the main characters in the | 714 | considered to have a happy ending if the situation of the main |
| 500 | novel improves towards the end or is constantly favorable. The novels were manually | 715 | characters in the novel improves towards the end or is |
| 501 | annotated with this information by domain experts. For feature extraction, the | 716 | constantly favorable. The novels were manually annotated with |
| 502 | authors first split each novel into <i>n</i> segments of the same | 717 | this information by domain experts. For feature extraction, the |
| 503 | length. They then calculate sentiment values for each of the segments by counting | 718 | authors first split each novel into <i>n</i> |
| 504 | the | 719 | segments of the same length. They then calculate sentiment |
| 505 | occurrences of words that appear in the respective segment and that are found in the | 720 | values for each of the segments based on a normalized word |
| 506 | German version of the <i>NRC Word-Emotion Association Lexicon</i><a id="fna53" class="fn" href="#fn53" title="Mohammad / Turney 2013, passim.">[53]</a> and divide this number by the | 721 | frequency with a German version of the <i>NRC Word-Emotion Association |
| 507 | length of the dictionary. Finally, they calculate the sentiment score for the | 722 | Lexicon</i>. |
| 508 | sections by taking the average of all sentiment scores in the segments that are part | 723 | <a id="fna60" class="fn" href="#fn60" title="Mohammad / Turney 2013.">[60]</a> An |
| 509 | of the section. These steps are then followed by classification with a support-vector | 724 | automatic sentiment classification with support vector machines |
| 510 | machine and the F1 score of 0.73, which the authors consider a good starting point | 725 | achieves reasonable and encouraging results. |
| 511 | for future work. | 726 | </p> |
| 512 | </p> | 727 | </div> |
| 513 | </div> | 728 | </div><a name="div16"> </a><div id="subchapter"><a name="hd14"> </a><h3> |
| 514 | </div> | ||
| 515 | <div id="subchapter"><a name="hd12"> </a><h3> | ||
| 516 | <div style="position:relative;width:90%;">4.2 Genre and Story-type Classification</div> | 729 | <div style="position:relative;width:90%;">4.2 Genre and Story-type Classification</div> |
| 517 | </h3> | 730 | </h3> |
| 518 | <p>The papers we have discussed so far focus on understanding the emotion associated | 731 | <p id="pid42"><span style="float: right; padding: .2em; color: #999; border: 1px solid #ccc; background-color: #eee; margin-right: -80px;">[<a href="#pid42">42</a>]</span>The papers we have discussed so far focus on understanding |
| 519 | with units of texts. This extracted information can further be used for downstream | 732 | the emotion associated with units of texts. This extracted |
| 520 | tasks and also for downstream evaluations. We discuss the following downstream | 733 | information can further be used for downstream tasks and |
| 521 | classification cases here. The papers in this category use sentiment and emotion | 734 | also for downstream evaluations. In the following, we |
| 522 | features for a higher-level classification, namely story-type clustering and literary | 735 | discuss downstream classification cases. The papers in this |
| 523 | genre classification. The assumption behind these works is that different types of | 736 | category use sentiment and emotion features for a |
| 524 | literary text may show different composition and distribution of emotion vocabulary | 737 | higher-level classification, namely story-type clustering |
| 525 | and thus can be classified based on this information. The hypothesis that different | 738 | and literary genre classification. The assumption behind |
| 526 | literary genres convey different emotions stems from common knowledge: we know that | 739 | these works is that different types of literary text may |
| 527 | horror stories instill <i>fear</i> and that mysteries evoke <i>anticipation</i> and <i>anger</i> while romances | 740 | show different composition and distribution of emotion |
| 528 | are filled with <i>joy</i> and <i>love</i>. However | 741 | vocabulary and thus can be classified based on this |
| 529 | as we will see in this section, the task of automatic classification of these genres | 742 | information. The hypothesis that different literary genres |
| 530 | is not always that straightforward and reliable. | 743 | convey different emotions stems from common knowledge: we |
| 531 | </p> | 744 | know that horror stories instill <i>fear</i> |
| 532 | <div id="subchapter"><a name="hd13"> </a><h3> | 745 | and that mysteries evoke <i>anticipation</i> |
| 746 | and <i>anger</i> while romances are filled | ||
| 747 | with <i>joy</i> and <i>love</i>. However as we will see in this section, the | ||
| 748 | task of automatic classification of these genres is not | ||
| 749 | always that straightforward and reliable. | ||
| 750 | </p><a name="div17"> </a><div id="subchapter"><a name="hd15"> </a><h3> | ||
| 533 | <div style="position:relative;width:90%;">4.2.1 Story-type clustering</div> | 751 | <div style="position:relative;width:90%;">4.2.1 Story-type clustering</div> |
| 534 | </h3> | 752 | </h3> |
| 535 | <p>Similarly to Zehe et al., Reagan et al.<a id="fna54" class="fn" href="#fn54" title="Reagan et al. 2016, passim.">[54]</a> are interested in automatically understanding a plot structure as a | 753 | <p id="pid43"><span style="float: right; padding: .2em; color: #999; border: 1px solid #ccc; background-color: #eee; margin-right: -80px;">[<a href="#pid43">43</a>]</span>Similarly to Zehe et al., Reagan et al.<a id="fna61" class="fn" href="#fn61" title="Reagan et al. 2016.">[61]</a> are |
| 536 | whole, not limited to a book ending. The inspiration for their work comes from Kurt | 754 | interested in automatically understanding a plot |
| 537 | Vonnegut’s lecture on emotional arcs of stories.<a id="fna55" class="fn" href="#fn55" title="Vonnegut 2010 (2005), passim.">[55]</a> | 755 | structure as a whole, but not limited to a book ending. |
| 538 | Reagan et al. test the idea that the plot | 756 | The inspiration for their work comes from Kurt |
| 539 | of each story can be plotted as an <i>emotional arc</i>, i.e. a time | 757 | Vonnegut’s lecture on emotional arcs of stories.<a id="fna62" class="fn" href="#fn62" title="Vonnegut 2010 (2005).">[62]</a> Reagan |
| 540 | series graph, where the <i>x</i>-axis represents a time point in a | 758 | et al. test the idea that the plot of each story can be |
| 541 | story, and the <i>y</i>-axis represents the events happening to the | 759 | visualized as an <i>emotional arc</i>, |
| 542 | main characters that can be favorable (peaks on a graph) or unfavorable (troughs on | 760 | i.e., a time series graph, where the <i>x</i>-axis represents a time point in a story, and |
| 543 | a | 761 | the <i>y</i>-axis represents the events |
| 544 | graph). As Vonnegut puts it, the stories can be grouped by these <i>arcs</i> and the number of such groupings is limited. To test this idea, Reagan | 762 | happening to the main characters that can be favorable |
| 545 | et al. collect the 1,327 most popular books from the <a href="https://www.gutenberg.org/" target="_blank">Project Gutenberg</a>.<a id="fna56" class="fn" href="#fn56" title="Project Gutenberg 1971-2019.">[56]</a> Each book is then split into segments for which | 763 | (peaks on a graph) or unfavorable (troughs on a graph). |
| 546 | sentiment scores (<i>happy</i> vs. <i>sad</i>) are | 764 | As Vonnegut puts it, the stories can be grouped by these |
| 547 | calculated and compared. The results of the analysis show support for six emotional | 765 | <i>arcs</i> and the number of such |
| 548 | patterns that are shared between subgroupings of the corpus: | 766 | groupings is limited. To test this idea, Reagan et al. |
| 549 | </p> | 767 | collect the 1,327 most popular books from the <a href="https://www.gutenberg.org/" target="_blank">Project |
| 550 | <ul class="ul_article"> | 768 | Gutenberg</a>.<a id="fna63" class="fn" href="#fn63" title="Project Gutenberg 1971–2019 [Webseite aus Deutschland nicht mehr erreichbar].">[63]</a> Each book is then split into |
| 551 | <li>Rise: the arc starts at a low point and steadily increases towards the end; </li> | 769 | segments for which happiness scores are calculated and |
| 552 | <li>Fall: the arc starts at a high point and steadily decreases towards the end; </li> | 770 | compared. The results of the analysis show support for |
| 553 | <li>Fall-rise: the arc drops in the middle of the story but increases towards the | 771 | six emotional patterns that are shared between |
| 554 | end; | 772 | subgroupings of the corpus. Additionally, Reagan et al. |
| 555 | </li> | 773 | find that some patterns are more popular among readers, |
| 556 | <li>Rise-fall: the arc hits the high point in the middle of the story and decreases | 774 | based on download counts, than others. |
| 557 | towards the end; | 775 | </p> |
| 558 | </li> | 776 | </div><a name="div18"> </a><div id="subchapter"><a name="hd16"> </a><h3> |
| 559 | <li>Rise-fall-rise: the arc fluctuates between high and low points but ends with an | ||
| 560 | increase; | ||
| 561 | </li> | ||
| 562 | <li>Fall-rise-fall: the arc fluctuates between high and low points but ends with a | ||
| 563 | decrease. | ||
| 564 | </li> | ||
| 565 | </ul> | ||
| 566 | <p>Additionally, Reagan et al. find that <i>Icarus</i>, <i>Oedipus</i>, and <i>Man in the hole</i> arcs are | ||
| 567 | the three most popular emotional arcs among readers, based on download counts. | ||
| 568 | </p> | ||
| 569 | </div> | ||
| 570 | <div id="subchapter"><a name="hd14"> </a><h3> | ||
| 571 | <div style="position:relative;width:90%;">4.2.2 Genre classification</div> | 777 | <div style="position:relative;width:90%;">4.2.2 Genre classification</div> |
| 572 | </h3> | 778 | </h3> |
| 573 | <p>There are other studies<a id="fna57" class="fn" href="#fn57" title="Samothrakis / Fasli 2015; Kim et al. 2017a; Kim et al. 2017b.">[57]</a> that are similar in spirit to the work done by | 779 | <p id="pid44"><span style="float: right; padding: .2em; color: #999; border: 1px solid #ccc; background-color: #eee; margin-right: -80px;">[<a href="#pid44">44</a>]</span>There are other studies<a id="fna64" class="fn" href="#fn64" title="Samothrakis / Fasli 2015; Kim et al. 2017a; Kim et al. 2017b.">[64]</a> that are similar in spirit to |
| 574 | Reagan. Samothrakis and Fasli examine the hypothesis that different genres clearly | 780 | the work done by Reagan et al. Samothrakis and Fasli |
| 575 | have different emotion patterns to reliably classify them with machine learning. To | 781 | examine the hypothesis that different genres clearly |
| 576 | that end, they collect works of the genres <i>mystery</i>, <i>humor</i>, <i>fantasy</i>, <i>horror</i>, <i>science fiction</i> and <i>western</i> from the Project Gutenberg. | 782 | have different emotion patterns to reliably classify |
| 577 | </p> | 783 | them with machine learning. To that end, they |
| 578 | <p>Using WordNet-Affect<a id="fna58" class="fn" href="#fn58" title="Strapparava / Valitutti 2004.">[58]</a> to | 784 | collect works of the genres <i>mystery</i>, <i>humor</i>, <i>fantasy</i>, <i>horror</i>, <i>science |
| 579 | detect emotion words as categorized by Ekman’s fundamental emotion classes, they | 785 | fiction</i> and <i>western</i> |
| 580 | calculate an emotion score for each sentence in the text. Each work is then | 786 | from the Project |
| 581 | transformed into six vectors, one for each basic emotion. A random forest classifier | 787 | Gutenberg. Using <a href="https://wndomains.fbk.eu/wnaffect.html" target="_blank">WordNet-Affect</a><a id="fna65" class="fn" href="#fn65" title="Strapparava / Valitutti 2004.">[65]</a> to detect emotion words as |
| 582 | achieves a classification accuracy of 0.52. This is significantly higher than a | 788 | categorized by Ekman’s fundamental emotion classes, |
| 583 | random baseline, which allows the authors to conclude that such a classification is | 789 | they calculate an emotion score for each sentence in |
| 584 | feasible. | 790 | the text. Each work is then transformed into six |
| 585 | </p> | 791 | vectors, one for each basic emotion. With a random |
| 586 | <p>A study by Kim et al.<a id="fna59" class="fn" href="#fn59" title="Kim et al. 2017a, passim.">[59]</a> originates from | 792 | forrest classifier, they show that genre |
| 587 | the same premise as the work by Samothrakis and Fasli but puts emphasis on finding | 793 | classification is possible based on this information |
| 588 | genre-specific correlations of emotion developments. Extending the set of tracked | 794 | with performance scores significantly above |
| 589 | emotions to Plutchik’s classification, Kim et al. collect 2,000 books from the | 795 | average. |
| 590 | Project Gutenberg that belong to five genres found in the Brown corpus<a id="fna60" class="fn" href="#fn60" title="Francis / Kucera 1979, passim.">[60]</a>, namely <i>adventure</i>, <i>science fiction</i>, <i>mystery</i>, <i>humor</i> and <i>romance</i>. | 796 | </p> |
| 591 | The authors extend the set of classification algorithms beyond random forests using | 797 | <p id="pid45"><span style="float: right; padding: .2em; color: #999; border: 1px solid #ccc; background-color: #eee; margin-right: -80px;">[<a href="#pid45">45</a>]</span>The study by Kim et al.<a id="fna66" class="fn" href="#fn66" title="Kim et al. 2017a.">[66]</a> originates from the same |
| 592 | a | 798 | premise as the work by Samothrakis and Fasli but |
| 593 | <span style="color:#035151"><i>multi-layer perceptron</i></span> and <span style="color:#035151"><i>convolutional | 799 | puts emphasis on finding genre-specific correlations |
| 594 | neural networks</i></span>, which achieves the best performance (0.59 F1-score). To | 800 | of emotion developments. They therefore link the |
| 595 | understand how uniform the emotion patterns in different genres are, the authors | 801 | motivation of Reagan et al. with the one by |
| 596 | introduce the notion of <i>prototypicality</i>, which is computed as | 802 | Samothrakis and Fasli. Extending the set of tracked |
| 597 | average of all emotion scores. Using this as a point of reference for each genre Kim | 803 | emotions to Plutchik’s classification, Kim et al. |
| 598 | et al. use Spearman correlation to calculate the uniformity of emotions per genre. | 804 | collect 2,000 books from the Project Gutenberg that |
| 599 | The results of this analysis suggest that <i>fear</i> and <i>anger</i> are the most salient plot devices in fiction, while <i>joy</i> is only of mediocre stability, which is in line with | 805 | belong to five genres found in the Brown corpus,<a id="fna67" class="fn" href="#fn67" title="Francis / Kucera 1979.">[67]</a> |
| 600 | findings of Samothrakis and Fasli. | 806 | namely <i>adventure</i>, <i>science fiction</i>, <i>mystery</i>, <i>humor</i> and <i>romance</i>. |
| 601 | </p> | 807 | The authors extend the set of classification |
| 602 | <p>The study by Henny-Krahmer<a id="fna61" class="fn" href="#fn61" title="Henny-Krahmer 2018, passim.">[61]</a> pursues | 808 | algorithms beyond random forests using a <span style="color:#035151"><i>multi-layer perceptron</i></span> and <span style="color:#035151"><i>convolutional neural networks</i></span>, |
| 603 | two goals: 1), to test whether different subgenres of Spanish American literature | 809 | which achieves the best performance. To understand |
| 604 | differ in degree and kind of emotionality, and 2), whether emotions in the novels | 810 | how uniform the emotion patterns in different genres |
| 605 | are | 811 | are, the authors introduce the notion of <i>prototypicality</i>, which is |
| 606 | expressed in direct speech of characters or in narrated text. To that end, they | 812 | computed as average of all emotion scores. Using |
| 607 | conduct a subgenre classification experiment on a corpus of Spanish American novels | 813 | this as a point of reference for each genre Kim et |
| 608 | using sentiment values as features. To answer the first question, each novel is split | 814 | al. use Spearman correlation to calculate the |
| 609 | into five segments and for each sentence in the segment the emotion score (polarity | 815 | uniformity of emotions per genre. The results of |
| 610 | values + Plutchik’s basic emotions) is calculated using SentiWordNet<a id="fna62" class="fn" href="#fn62" title="Baccianella et al. 2010.">[62]</a> and NRC<a id="fna63" class="fn" href="#fn63" title="Mohammad / Turney 2013.">[63]</a> dictionaries. The classifier achieves an average F1 | 816 | this analysis suggest that <i>fear</i> and <i>anger</i> are |
| 611 | of 0.52, which is higher than the most-frequent class baseline and, hence, provides | 817 | the most salient plot devices in fiction, while <i>joy</i> is only of mediocre |
| 612 | a | 818 | stability, which is in line with findings of |
| 613 | support for emotion-based features in subgenre classification. The analysis of | 819 | Samothrakis and Fasli. |
| 614 | feature importance shows that the most salient features come from the sentiment | 820 | </p> |
| 615 | scores calculated from the characters’ direct speech and that novels with higher | 821 | <p id="pid46"><span style="float: right; padding: .2em; color: #999; border: 1px solid #ccc; background-color: #eee; margin-right: -80px;">[<a href="#pid46">46</a>]</span>The study by Henny-Khramer<a id="fna68" class="fn" href="#fn68" title="Henny-Krahmer 2018.">[68]</a> pursues two goals: 1), |
| 616 | values of positive speech are more likely to be sentimental novels. | 822 | to test whether different subgenres of Spanish |
| 617 | </p> | 823 | American literature differ in degree and kind of |
| 618 | <p>There are some limitations to the studies presented in this section. On the one hand, | 824 | emotionality, and 2), whether emotions in the novels |
| 619 | it is questionable how reliable <span style="color:#035151"><i>coarse emotion scoring</i></span> is that | 825 | are expressed in direct speech of characters or in |
| 620 | takes into account only presence or absence of words found in specialized | 826 | narrated text. To that end, they conduct a subgenre |
| 621 | dictionaries and overlooks negations and modifiers that can either negate an emotion | 827 | classification experiment on a corpus of Spanish |
| 622 | word or increase/decrease its intensity. On the other hand, a limited view of the | 828 | American novels using sentiment values as features. |
| 623 | emotional content as a sum of emotion bearing words reserves no room for qualitative | 829 | To answer the first question, each novel is split |
| 624 | interpretation of the texts – it is not clear how one can distinguish between emotion | 830 | into five segments and for each sentence in the |
| 625 | words used by the author to express their sentiment, between words used to describe | 831 | segment the emotion score (polarity values + |
| 626 | characters’ feelings, and emotion words that characters use to address or describe | 832 | Plutchik’s basic emotions) is calculated using <a href="https://github.com/aesuli/SentiWordNet" target="_blank">SentiWordNet</a><a id="fna69" class="fn" href="#fn69" title="Baccianella et al. 2010.">[69]</a> and <a href="https://saifmohammad.com/WebPages/NRC-Emotion-Lexicon.htm" target="_blank">NRC</a><a id="fna70" class="fn" href="#fn70" title="Mohammad / Turney 2013.">[70]</a> dictionaries. The analysis of feature |
| 627 | other characters in a story. | 833 | importance shows that the most salient features come |
| 628 | </p> | 834 | from the sentiment scores calculated from the |
| 629 | </div> | 835 | characters’ direct speech and that novels with |
| 630 | </div> | 836 | higher values of positive speech are more likely to |
| 631 | <div id="subchapter"><a name="hd15"> </a><h3> | 837 | be sentimental novels. This is an interesting |
| 632 | <div style="position:relative;width:90%;">4.3 Temporal Change of Sentiment</div> | 838 | variant of the beforehand mentioned studies – it is |
| 839 | important to distinguish characters' speech from | ||
| 840 | other parts of the text. | ||
| 841 | </p> | ||
| 842 | <p id="pid47"><span style="float: right; padding: .2em; color: #999; border: 1px solid #ccc; background-color: #eee; margin-right: -80px;">[<a href="#pid47">47</a>]</span>There are some limitations to the studies presented | ||
| 843 | in this section. On the one hand, it is questionable | ||
| 844 | how reliable <span style="color:#035151"><i>coarse emotion | ||
| 845 | scoring</i></span> is that takes into account only | ||
| 846 | presence or absence of words found in specialized | ||
| 847 | dictionaries and overlooks negations and modifiers | ||
| 848 | that can either negate an emotion word or | ||
| 849 | increase/decrease its intensity. On the other hand, | ||
| 850 | a limited view of the emotional content as a sum of | ||
| 851 | emotion bearing words reserves no room for | ||
| 852 | qualitative interpretation of the texts – it is not | ||
| 853 | clear how one can distinguish between emotion words | ||
| 854 | used by the author to express their sentiment, | ||
| 855 | between words used to describe characters’ feelings, | ||
| 856 | and emotion words that characters use to address or | ||
| 857 | describe other characters in a story. | ||
| 858 | </p> | ||
| 859 | </div> | ||
| 860 | </div><a name="div19"> </a><div id="subchapter"><a name="hd17"> </a><h3> | ||
| 861 | <div style="position:relative;width:90%;">4.3 Structural Changes of Sentiment</div> | ||
| 633 | </h3> | 862 | </h3> |
| 634 | <p>The papers that we have reviewed so far approach the problem of sentiment and emotion | 863 | <p id="pid48"><span style="float: right; padding: .2em; color: #999; border: 1px solid #ccc; background-color: #eee; margin-right: -80px;">[<a href="#pid48">48</a>]</span>The papers that we have reviewed so far approach |
| 635 | analysis as a classification task. However, applications of sentiment analysis are | 864 | the problem of sentiment and emotion analysis as a |
| 636 | not only limited to classification. In other fields, for example computational social | 865 | classification task. However, applications of |
| 637 | sciences, sentiment analysis can be used for analyzing political preferences of the | 866 | sentiment analysis are not only limited to |
| 638 | electorate or for mining opinions about different products or topics. Similarly, | 867 | classification. In other fields, for example |
| 639 | several digital humanities studies incorporate sentiment analysis methods in a task | 868 | computational social sciences, sentiment analysis |
| 640 | of mining sentiments and emotions of people who lived in the past. The goal of these | 869 | can be used for analyzing political preferences of |
| 641 | studies is not only to recognize sentiments, but also to understand how they were | 870 | the electorate or for mining opinions about |
| 871 | different products or topics. Similarly, several | ||
| 872 | digital humanities studies incorporate sentiment | ||
| 873 | analysis methods in a task of mining sentiments | ||
| 874 | and emotions of people who lived in the past. The | ||
| 875 | goal of these studies is not only to recognize | ||
| 876 | sentiments, but also to understand how they were | ||
| 642 | formed. | 877 | formed. |
| 643 | </p> | 878 | </p><a name="div20"> </a><div id="subchapter"><a name="hd18"> </a><h3> |
| 644 | <div id="subchapter"><a name="hd16"> </a><h3> | ||
| 645 | <div style="position:relative;width:90%;">4.3.1 Topography of emotions</div> | 879 | <div style="position:relative;width:90%;">4.3.1 Topography of emotions</div> |
| 646 | </h3> | 880 | </h3> |
| 647 | <p>Heuser et al.<a id="fna64" class="fn" href="#fn64" title="Heuser et al. 2016, passim.">[64]</a> start with a premise | 881 | <p id="pid49"><span style="float: right; padding: .2em; color: #999; border: 1px solid #ccc; background-color: #eee; margin-right: -80px;">[<a href="#pid49">49</a>]</span>Heuser et al.<a id="fna71" class="fn" href="#fn71" title="Heuser et al. 2016.">[71]</a> start with a premise that |
| 648 | that emotions occur at a specific moment in time and space, thus making it possible | 882 | emotions occur at a specific moment in time and |
| 649 | to link emotions to specific geographical locations. Consequently, having such | 883 | space, thus making it possible to link emotions to |
| 650 | information at hand, one can understand which emotions are hidden behind certain | 884 | specific geographical locations. Consequently, |
| 651 | landmarks. As a <span style="color:#035151"><i>proof-of-concept</i></span>, Heuser et al. build an interactive map, <a href="https://www.historypin.org/en/victorian-london/" target="_blank"> | 885 | having such information at hand, one can |
| 652 | Mapping emotions in Victorian London</a><a id="fna65" class="fn" href="#fn65" title="Historypin 2010-2017.">[65]</a>, where each location is tagged with emotion | 886 | understand which emotions are hidden behind |
| 653 | labels. To construct a corpus for their analysis, Heuser et al. collect a large | 887 | certain landmarks. As a <span style="color:#035151"><i>proof-of-concept</i></span>, Heuser et al. build an |
| 654 | corpus of English books from the eighteenth and nineteenth century and extract 383 | 888 | <a href="https://www.historypin.org/en/victorian-london/" target="_blank"> interactive map of emotions</a> |
| 655 | geographical locations of London that have at least ten mentions each. The resulting | 889 | in Victorian London<a id="fna72" class="fn" href="#fn72" title="Historypin 2010–2017.">[72]</a> |
| 656 | corpus includes 15,000 passages, each of which has a toponym in the middle and 100 | 890 | where each location is tagged with emotion labels. |
| 657 | words directly preceding and following the location mention. The data is then given | 891 | The underlying corpus for their analysis consists |
| 658 | to annotators who are asked to define whether each of the passages expressed <i>happiness</i> or <i>fear</i>, or <i>neutrality</i>. The same data is also analyzed by a custom sentiment analysis | 892 | of English books from the eighteenth and |
| 659 | program that would assign each passage one of these emotion categories. | 893 | nineteenth century, from which they extract |
| 660 | </p> | 894 | frequently mentioned geographical locations of |
| 661 | <p>Some striking observations are made with regard to the data analysis. First, there | 895 | London. The presegmented data is then given to |
| 662 | is | 896 | annotators who are asked to define whether each of |
| 663 | a clear discrepancy between fiction and reality – while toponyms from the West End | 897 | the passages expressed <i>happiness</i> or <i>fear</i>, or |
| 664 | with Westminster and the City are over-represented in the books, the same does not | 898 | <i>neutrality</i>. The same data |
| 665 | hold true for the East End with Tower Hamlets, Southwark, and Hackney. Hence, there | 899 | is further analyzed with a dictionary-based |
| 666 | is less information about emotions pertaining to these particular London locations. | 900 | sentiment classifier. |
| 667 | Another striking detail is that the resulting map is dominated by the neutral | 901 | </p> |
| 668 | emotion. Heuser et al. argue that this has nothing to do with the absence of emotions | 902 | <p id="pid50"><span style="float: right; padding: .2em; color: #999; border: 1px solid #ccc; background-color: #eee; margin-right: -80px;">[<a href="#pid50">50</a>]</span>Some striking observations are made with regard |
| 669 | but rather stems from the fact that emotions tend to be silenced in public domain, | 903 | to the data analysis. First, there is a clear |
| 904 | discrepancy between fiction and reality – while | ||
| 905 | toponyms from the West End with Westminster and | ||
| 906 | the City are over-represented in the books, the | ||
| 907 | same does not hold true for the East End with | ||
| 908 | Tower Hamlets, Southwark, and Hackney. Hence, | ||
| 909 | there is less information about emotions | ||
| 910 | pertaining to these particular London locations. | ||
| 911 | Another striking detail is that the resulting map | ||
| 912 | is dominated by the neutral emotion. Heuser et al. | ||
| 913 | argue that this has nothing to do with the absence | ||
| 914 | of emotions but rather stems from the fact that | ||
| 915 | emotions tend to be silenced in public domain, | ||
| 670 | which influenced the annotators decision. | 916 | which influenced the annotators decision. |
| 671 | </p> | 917 | </p> |
| 672 | <p>The space and time context are also used by Bruggman and Fabrikant<a id="fna66" class="fn" href="#fn66" title="Bruggmann / Fabrikant 2014, passim.">[66]</a> who model sentiments of Swiss | 918 | <p id="pid51"><span style="float: right; padding: .2em; color: #999; border: 1px solid #ccc; background-color: #eee; margin-right: -80px;">[<a href="#pid51">51</a>]</span>The space and time context are also used by |
| 673 | historians towards places in Switzerland in different historical periods. As the | 919 | Bruggman and Fabrikant<a id="fna73" class="fn" href="#fn73" title="Bruggmann / Fabrikant 2014.">[73]</a> who model |
| 674 | authors note, it is unlikely that a historian will directly express attitudes towards | 920 | sentiments of Swiss historians towards places in |
| 675 | certain toponyms, but it is very likely that words they use to describe those can | 921 | Switzerland in different historical periods. As |
| 676 | bear some negative connotation (e.g. cholera, death). Correspondingly, such places | 922 | the authors note, it is unlikely that a historian |
| 677 | should be identified as bearing negative sentiment by a sentiment analysis tool. | 923 | will directly express attitudes towards certain |
| 678 | Additionally, they study the changes of sentiment towards a particular place over | 924 | toponyms, but it is very likely that words they |
| 679 | time. Using the <i>General Inquirer</i> (GI) lexicon<a id="fna67" class="fn" href="#fn67" title="Stone et al. 1968.">[67]</a> to identify | 925 | use to describe those can bear some negative |
| 680 | positive and negative terms in the document, they assign each document a sentiment | 926 | connotation (e.g. cholera, death). |
| 681 | score by summing up the weights of negative and positive words and normalizing them | 927 | Correspondingly, such places should be identified |
| 682 | by the document length. The authors conclude that the results of their analysis look | 928 | as bearing negative sentiment by a sentiment |
| 683 | promising, especially regarding negatively scored articles. However, the authors find | 929 | analysis tool. Additionally, they study the |
| 684 | difficulties in interpreting positively ranked documents, which may be due to the | 930 | changes of sentiment towards a particular place |
| 685 | fact that negative information is more salient. | 931 | over time. Using the <i>General Inquirer</i> (GI) lexicon<a id="fna74" class="fn" href="#fn74" title="Stone et al. 1968.">[74]</a> to identify positive and |
| 686 | </p> | 932 | negative terms in the document, they assign |
| 687 | </div> | 933 | sentiment scores and conclude that the results of |
| 688 | <div id="subchapter"><a name="hd17"> </a><h3> | 934 | their analysis look promising, especially |
| 935 | regarding negatively scored articles. | ||
| 936 | </p> | ||
| 937 | </div><a name="div21"> </a><div id="subchapter"><a name="hd19"> </a><h3> | ||
| 689 | <div style="position:relative;width:90%;">4.3.2 Tracking sentiment</div> | 938 | <div style="position:relative;width:90%;">4.3.2 Tracking sentiment</div> |
| 690 | </h3> | 939 | </h3> |
| 691 | <p>Other papers in this category link sentiment and emotion to certain groups, rather | 940 | <p id="pid52"><span style="float: right; padding: .2em; color: #999; border: 1px solid #ccc; background-color: #eee; margin-right: -80px;">[<a href="#pid52">52</a>]</span>Other papers in this category link sentiment |
| 692 | than geographical locations. The goal of these studies is to understand how sentiment | 941 | and emotion to certain groups, rather than |
| 693 | within and towards these groups was formed. | 942 | geographical locations. The goal of these studies |
| 694 | </p> | 943 | is to understand how sentiment within and towards |
| 695 | <p>Taboada et al.<a id="fna68" class="fn" href="#fn68" title="Taboada et al. 2006, passim; Taboada et al. 2008, passim.">[68]</a> | 944 | these groups was formed. |
| 696 | aim at tracking the literary reputation of six authors writing in the first half of | 945 | </p> |
| 697 | the twentieth century. The research questions raised in the project are how the | 946 | <p id="pid53"><span style="float: right; padding: .2em; color: #999; border: 1px solid #ccc; background-color: #eee; margin-right: -80px;">[<a href="#pid53">53</a>]</span>Taboada et al.<a id="fna75" class="fn" href="#fn75" title="Taboada et al. 2006; Taboada et al. 2008.">[75]</a> aim at |
| 698 | reputation is made or lost, and how to find correlation between what is written about | 947 | tracking the literary reputation of six authors |
| 699 | the author and their work to the author’s reputation and subsequent canonicity. To | 948 | writing in the first half of the twentieth |
| 700 | that end, the project’s goal is to examine critical reviews of six authors’ writing | 949 | century. The research questions raised in the |
| 701 | and to map information contained in texts critical to the author’s reputation. The | 950 | project are how the reputation is made or lost, |
| 702 | material they work with includes not only reviews, but also press notes, press | 951 | and how to find correlation between what is |
| 703 | articles, and letters to editors (including from the authors themselves). For the | 952 | written about the authors and their work to the |
| 704 | pilot project with Galsworthy and Lawrence they collected and scanned 330 documents | 953 | authors’ reputation and subsequent canonicity. The |
| 705 | (480,000 words). The documents are tagged for the parts of speech and relevant words | 954 | project’s goal is to examine critical reviews of |
| 706 | (positive and negative) are extracted using custom-made sentiment dictionaries. The | 955 | six authors’ writing and to map information |
| 707 | sentiment orientation of rhetorically important parts of the texts is then measured. | 956 | contained in texts critical to the author’s |
| 708 | 957 | reputation. The material they work with includes | |
| 709 | </p> | 958 | not only reviews, but also press notes, press |
| 710 | <p>Chen et al.<a id="fna69" class="fn" href="#fn69" title="Chen et al. 2012, passim.">[69]</a> aim to understand personal | 959 | articles, and letters to editors (including from |
| 711 | narratives of Korean <i>comfort women</i> who had been forced into | 960 | the authors themselves). They collected and |
| 712 | sexual slavery by Japanese military during World War II. Adapting the <i>WordNet-Affect</i> lexicon,<a id="fna70" class="fn" href="#fn70" title="Strapparava / Valitutti 2004.">[70]</a> Chen et | 961 | scanned 330 documents and tagged them with |
| 713 | al. build their own emotion dictionary to spot emotional keywords in women’s stories | 962 | polarity words with custom-made sentiment |
| 714 | and map the sentences to emotion categories. By adding variables of time and space, | 963 | dictionaries. The sentiment orientation of |
| 715 | Chen et al. provide a unified framework of collective remembering of this historical | 964 | rhetorically important parts of the texts is then |
| 716 | event as witnessed by the victims. | 965 | measured. The authors conclude that the current |
| 717 | </p> | 966 | approach has mostly been limited by a |
| 718 | <p>Finally, an interesting project to follow is the <a href="https://oceanicexchanges.org/" target="_blank">Oceanic Exchanges</a><a id="fna71" class="fn" href="#fn71" title="Oceanic Exchanges 2017.">[71]</a> project that started in late 2017. One goal of the project is | 967 | non-sufficiently large lexicon. |
| 719 | to trace information exchange in nineteenth-century newspapers and journals using | 968 | </p> |
| 720 | sentiment as one of the variables under analysis. | 969 | <p id="pid54"><span style="float: right; padding: .2em; color: #999; border: 1px solid #ccc; background-color: #eee; margin-right: -80px;">[<a href="#pid54">54</a>]</span>Chen et al.<a id="fna76" class="fn" href="#fn76" title="Chen et al. 2012.">[76]</a> aim to understand personal narratives |
| 721 | </p> | 970 | of Korean <i>comfort women</i> who |
| 722 | </div> | 971 | had been forced into sexual slavery by Japanese |
| 723 | <div id="subchapter"><a name="hd18"> </a><h3> | 972 | military during World War II. Adapting the <i>WordNet-Affect</i> lexicon,<a id="fna77" class="fn" href="#fn77" title="Strapparava / Valitutti 2004.">[77]</a> Chen et al. build their |
| 724 | <div style="position:relative;width:90%;">4.3.3 Sentiment recognition in historical texts</div> | 973 | own emotion dictionary to spot keywords in women’s |
| 974 | stories and map the sentences to emotion | ||
| 975 | categories. By adding variables of time and space, | ||
| 976 | Chen et al. provide a unified framework of | ||
| 977 | collective remembering of this historical event as | ||
| 978 | witnessed by the victims. | ||
| 979 | </p> | ||
| 980 | <p id="pid55"><span style="float: right; padding: .2em; color: #999; border: 1px solid #ccc; background-color: #eee; margin-right: -80px;">[<a href="#pid55">55</a>]</span>An interesting methodological contribution has | ||
| 981 | been made by Gao et al.<a id="fna78" class="fn" href="#fn78" title="Geo et al. 2016.">[78]</a> Instead of using raw counts of | ||
| 982 | polarity words over time, they propose that | ||
| 983 | filters are used to smooth the time series, which | ||
| 984 | further allows for other downstream | ||
| 985 | applications. | ||
| 986 | </p> | ||
| 987 | </div><a name="div22"> </a><div id="subchapter"><a name="hd20"> </a><h3> | ||
| 988 | <div style="position:relative;width:90%;">4.3.3 Sentiment recognition in historical | ||
| 989 | texts | ||
| 990 | </div> | ||
| 725 | </h3> | 991 | </h3> |
| 726 | <p>Other papers put emphasis not so much on the sentiments expressed by writers but | 992 | <p id="pid56"><span style="float: right; padding: .2em; color: #999; border: 1px solid #ccc; background-color: #eee; margin-right: -80px;">[<a href="#pid56">56</a>]</span>Other papers put emphasis not so much on the |
| 727 | instead focus on the particularities of historical language. | 993 | sentiments expressed by writers but instead focus |
| 728 | </p> | 994 | on the particularities of historical language. |
| 729 | <p>Marchetti et al.<a id="fna72" class="fn" href="#fn72" title="Marchetti et al. 2014, passim.">[72]</a> and Sprugnoli et | 995 | Marchetti et al.<a id="fna79" class="fn" href="#fn79" title="Marchetti et al. 2014.">[79]</a> and Sprugnoli et al.<a id="fna80" class="fn" href="#fn80" title="Sprugnoli et al. 2016.">[80]</a> |
| 730 | al. | 996 | present the integration of sentiment analysis in |
| 731 | <a id="fna73" class="fn" href="#fn73" title="Sprugnoli et al. 2016, passim.">[73]</a> present the integration of | 997 | the <a href="https://alcidedigitale.fbk.eu/" target="_blank">ALCIDE</a> (Analysis of Language and Content in |
| 732 | sentiment analysis in the <a href="http://celct.fbk.eu:8080/Alcide_Demo/" target="_blank">ALCIDE</a> (Analysis of Language and Content In a Digital Environment) | 998 | a Digital Environment) project.<a id="fna81" class="fn" href="#fn81" title="ALCIDE Demo 2014–2015.">[81]</a> The sentiment |
| 733 | project<a id="fna74" class="fn" href="#fn74" title="ALCIDE Demo 2014-2015.">[74]</a>. The sentiment analysis module is | 999 | analysis module is based on <span style="color:#035151"><i>WordNet-Affect</i></span>, <span style="color:#035151"><i>SentiWordNet</i></span><a id="fna82" class="fn" href="#fn82" title="Baccianella et al. 2010.">[82]</a> and <span style="color:#035151"><i>MultiWordNet</i></span>.<a id="fna83" class="fn" href="#fn83" title="Pianta et al. 2002.">[83]</a> Each document is assigned a |
| 734 | based on <span style="color:#035151"><i>WordNet-Affect</i></span>, <span style="color:#035151"><i>SentiWordNet</i></span><a id="fna75" class="fn" href="#fn75" title="Baccianella et al. 2010, passim.">[75]</a> and <span style="color:#035151"><i>MultiWordNet</i></span>.<a id="fna76" class="fn" href="#fn76" title="Pianta et al. 2002, passim.">[76]</a> Each | 1000 | normalized polarity score. The overall conclusion |
| 735 | document is assigned a polarity score by summing up the words with prior polarity | 1001 | of their work is that the assignment of a polarity |
| 736 | and | 1002 | in the historical domain is a challenging task |
| 737 | dividing by the number of words in the document. A positive global score leads to | 1003 | largely due to lack of agreement on polarity of |
| 738 | a | 1004 | historical sources between human annotators. |
| 739 | positive document polarity and a negative global score leads to a negative document | 1005 | </p> |
| 740 | polarity. The overall conclusion of their work is that the assignment of a polarity | 1006 | <p id="pid57"><span style="float: right; padding: .2em; color: #999; border: 1px solid #ccc; background-color: #eee; margin-right: -80px;">[<a href="#pid57">57</a>]</span>Challenged by the problem of applicability of |
| 741 | in the historical domain is a challenging task largely due to lack of agreement on | 1007 | existing emotion lexicons to historical texts, |
| 742 | polarity of historical sources between human annotators. | 1008 | Buechel et al.<a id="fna84" class="fn" href="#fn84" title="Buechel et al. 2017.">[84]</a> propose a new method of |
| 743 | </p> | 1009 | constructing affective lexicons that would adapt |
| 744 | <p>Challenged by the problem of applicability of existing emotion lexicons to historical | 1010 | well to German texts written up to three centuries |
| 745 | texts, Buechel et al.<a id="fna77" class="fn" href="#fn77" title="Buechel et al. 2017, passim.">[77]</a> propose a new | 1011 | ago. In their study, Buechel et al. use the |
| 746 | method of constructing affective lexicons that would adapt well to German texts | 1012 | representation of affect based on the <span style="color:#035151"><i>Valence-Arousal-Dominance model</i></span> |
| 747 | written up to three centuries ago. In their study, Buechel et al. use the | 1013 | (an adaptation of Russel’s circumplex model, see |
| 748 | representation of affect based on the <span style="color:#035151"><i>Valence-Arousal-Dominance | 1014 | <a title="" href="#hd8">section |
| 749 | model</i></span> (an adaptation of Russel’s circumplex model, see <a title="" href="#hd6">Section 2.3</a>). | 1015 | 2.3</a>). Presumably, such a representation |
| 750 | Presumably, such a representation provides a finer-grained insight into the literary | 1016 | provides a finer-grained insight into the literary |
| 751 | text,<a id="fna78" class="fn" href="#fn78" title="Buechel et al. 2016, p. 54, p. 59.">[78]</a> which is more expressive | 1017 | text,<a id="fna85" class="fn" href="#fn85" title="Buechel et al. 2016 p. 54, p. 59.">[85]</a>, which is more expressive than |
| 752 | than discrete categories, as it quantifies the emotion along three different | 1018 | discrete categories, as it quantifies the emotion |
| 753 | dimensions. As a basis for the analysis, they collect German texts from the <a href="http://www.deutschestextarchiv.de/" target="_blank">Deutsches Textarchiv</a><a id="fna79" class="fn" href="#fn79" title="Deutsches Textarchiv 2007-2019.">[79]</a> written | 1019 | along three different dimensions. As a basis for |
| 754 | between 1690 and 1899. The corpus is split into seven slices, each spanning 30 years. | 1020 | the analysis, they collect German texts from the |
| 755 | For each slice they compute word similarities and obtain seven distinct emotion | 1021 | <a href="http://www.deutschestextarchiv.de/" target="_blank">Deutsches Textarchiv</a><a id="fna86" class="fn" href="#fn86" title="Deutsches Textarchiv 2007–2019.">[86]</a> written between 1690 |
| 756 | lexicons, each corresponding to specific time period. This allows for, the authors | 1022 | and 1899. The corpus is split into seven slices, |
| 757 | argue, the tracing of the shift in emotion association of words over time. | 1023 | each spanning 30 years. For each slice they |
| 758 | </p> | 1024 | compute word similarities and obtain seven |
| 759 | <p>Finally, Leemans et al.<a id="fna80" class="fn" href="#fn80" title="Leemans et al. 2017, passim.">[80]</a> aim to | 1025 | distinct emotion lexicons, each corresponding to |
| 760 | trace historical changes in emotion expressions and to develop methods to trace these | 1026 | specific time period. This allows for, the authors |
| 761 | changes in a corpus of 29 Dutch language theatre plays written between 1600 and 1800. | 1027 | argue, the tracing of the shift in emotion |
| 762 | Expanding the Dutch version of <i>Linguistic Inquiry and Word Count</i> (LIWC) dictionary<a id="fna81" class="fn" href="#fn81" title="Pennebaker et al. 2007.">[81]</a> with | 1028 | association of words over time. |
| 763 | historical terms, the authors are able to increase the recall of emotion recognition | 1029 | </p> |
| 764 | with a dictionary. In addition, they develop a fine-grained vocabulary mapping body | 1030 | <p id="pid58"><span style="float: right; padding: .2em; color: #999; border: 1px solid #ccc; background-color: #eee; margin-right: -80px;">[<a href="#pid58">58</a>]</span>Finally, Leemans et al.<a id="fna87" class="fn" href="#fn87" title="Leemans et al. 2017.">[87]</a> aim to trace |
| 765 | terms to emotions, and show that a combination of LIWC and their lexicon lead to | 1031 | historical changes in emotion expressions and to |
| 1032 | develop methods to trace these changes in a corpus | ||
| 1033 | of 29 Dutch language theatre plays written between | ||
| 1034 | 1600 and 1800. Expanding the Dutch version of <i>Linguistic Inquiry and Word | ||
| 1035 | Count</i> (LIWC) dictionary<a id="fna88" class="fn" href="#fn88" title="Pennebaker et al. 2007.">[88]</a> with historical | ||
| 1036 | terms, the authors are able to increase the recall | ||
| 1037 | of emotion recognition with a dictionary. In | ||
| 1038 | addition, they develop a fine-grained vocabulary | ||
| 1039 | mapping body terms to emotions, and show that a | ||
| 1040 | combination of LIWC and their lexicon lead to | ||
| 766 | improvement in the emotion recognition. | 1041 | improvement in the emotion recognition. |
| 768 | </div> | 1043 | </div> |
| 769 | </div> | 1044 | </div><a name="div23"> </a><div id="subchapter"><a name="hd21"> </a><h3> |
| 770 | <div id="subchapter"><a name="hd19"> </a><h3> | 1045 | <div style="position:relative;width:90%;">4.4 Character Network Analysis and |
| 771 | <div style="position:relative;width:90%;">4.4 Character Network Analysis and Relationship Extraction</div> | 1046 | Relationship Extraction |
| 1047 | </div> | ||
| 772 | </h3> | 1048 | </h3> |
| 773 | <p>The papers reviewed above address sentiment analysis of literary texts mainly on a | 1049 | <p id="pid59"><span style="float: right; padding: .2em; color: #999; border: 1px solid #ccc; background-color: #eee; margin-right: -80px;">[<a href="#pid59">59</a>]</span>The papers reviewed above address sentiment |
| 774 | document level. This abstraction is warranted if the goal is to get an insight into | 1050 | analysis of literary texts mainly on a document |
| 775 | the distribution of emotions in a corpus of books. However, emotions depicted in | 1051 | level. This abstraction is warranted if the goal |
| 776 | books do not exist in isolation but are associated with characters who are at the | 1052 | is to get an insight into the distribution of |
| 777 | core of any literary narrative.<a id="fna82" class="fn" href="#fn82" title="Ingermanson / Economy 2009, p. 107.">[82]</a> This leads us to ask what sentiment and emotion analysis can tell us | 1053 | emotions in a corpus of books. However, emotions |
| 778 | about the characters. How emotional are they? And what role do emotions play in their | 1054 | depicted in books do not exist in isolation but |
| 779 | interaction? | 1055 | are associated with characters who are at the core |
| 780 | </p> | 1056 | of any literary narrative.<a id="fna89" class="fn" href="#fn89" title="Ingermanson / Economy 2009, p. 107.">[89]</a> This |
| 781 | <p>Character relationships have been analyzed in computational linguistics from a graph | 1057 | leads us to ask what sentiment and emotion |
| 782 | theoretic perspective, particularly using social network analysis.<a id="fna83" class="fn" href="#fn83" title="Agarwal et al. 2013; Elson et al. 2011.">[83]</a> Fewer works, | 1058 | analysis can tell us about the characters. How |
| 783 | however, address the problem of modeling character relationships in terms of | 1059 | emotional are they? And what role do emotions play |
| 784 | sentiment. Below we provide an overview of several papers that propose the | 1060 | in their interaction? |
| 785 | methodology for extracting this information. | 1061 | </p> |
| 786 | </p> | 1062 | <p id="pid60"><span style="float: right; padding: .2em; color: #999; border: 1px solid #ccc; background-color: #eee; margin-right: -80px;">[<a href="#pid60">60</a>]</span>Character relationships have been analyzed in |
| 787 | <div id="subchapter"><a name="hd20"> </a><h3> | 1063 | computational linguistics from a graph theoretic |
| 788 | <div style="position:relative;width:90%;">4.4.1 Sentiment dynamics between characters</div> | 1064 | perspective, particularly using social network |
| 1065 | analysis.<a id="fna90" class="fn" href="#fn90" title="Agarwal et al. 2013; Elson et al. 2011.">[90]</a> Fewer works, | ||
| 1066 | however, address the problem of modeling character | ||
| 1067 | relationships in terms of sentiment. Below we | ||
| 1068 | provide an overview of several papers that propose | ||
| 1069 | the methodology for extracting this information. | ||
| 1070 | </p><a name="div24"> </a><div id="subchapter"><a name="hd22"> </a><h3> | ||
| 1071 | <div style="position:relative;width:90%;">4.4.1 Sentiment dynamics between | ||
| 1072 | characters | ||
| 1073 | </div> | ||
| 789 | </h3> | 1074 | </h3> |
| 790 | <p>Several studies present automatic methods for analyzing sentiment dynamics between | 1075 | <p id="pid61"><span style="float: right; padding: .2em; color: #999; border: 1px solid #ccc; background-color: #eee; margin-right: -80px;">[<a href="#pid61">61</a>]</span>Several studies present automatic methods for |
| 791 | plays’ characters. The goal of the study by Nalisnick and Baird<a id="fna84" class="fn" href="#fn84" title="Nalisnick / Baird 2013a, passim.">[84]</a> is to track the emotional trajectories of | 1076 | analyzing sentiment dynamics between plays’ |
| 792 | interpersonal relationships. The structured format of a dialog allows them to | 1077 | characters. The goal of the study by Nalisnick and |
| 793 | identify who is speaking to whom, which makes it possible to mine | 1078 | Baird<a id="fna91" class="fn" href="#fn91" title="Nalisnick / Baird 2013a.">[91]</a> is to track the emotional |
| 794 | character-to-character sentiment by summing the valence values of words that appear | 1079 | trajectories of interpersonal relationships. The |
| 795 | in the continuous direct speech and are found in the lexicon<a id="fna85" class="fn" href="#fn85" title="Nielsen 2011, passim.">[85]</a> | 1080 | structured format of a dialog allows them to |
| 796 | of affective norms. The extension<a id="fna86" class="fn" href="#fn86" title="Nalisnick / Baird 2013b, passim.">[86]</a> of the previous research from the same authors | 1081 | identify who is speaking to whom, which makes it |
| 797 | introduces the concept of a sentiment network, a dynamic social network of | 1082 | possible to mine character-to-character sentiment |
| 798 | characters. Changing polarities between characters are modeled as edge weights in | 1083 | by summing the valence values of words that appear |
| 799 | the | 1084 | in the continuous direct speech and are found in |
| 800 | network. Motivated by the desire to explain such networks in terms of a general | 1085 | the lexicon<a id="fna92" class="fn" href="#fn92" title="Nielsen 2011.">[92]</a> of affective norms. The |
| 801 | sociological model, Nalisnick and Baird test whether Shakespeare’s plays obey the | 1086 | extension<a id="fna93" class="fn" href="#fn93" title="Nalisnick / Baird 2013b.">[93]</a> of the previous research from the |
| 802 | <span style="color:#035151"><i>Structural Balance Theory</i></span> by Marvel et al.<a id="fna87" class="fn" href="#fn87" title="Marvel et al. 2011.">[87]</a> that postulates that a friend of a | 1087 | same authors introduces the concept of a |
| 803 | friend is also your friend. Using the procedure proposed by Marvel et al. on their | 1088 | »sentiment network«, a dynamic social |
| 804 | Shakespearean sentiment networks, Nalisnick and Baird test whether they can predict | 1089 | network of characters. Changing polarities between |
| 805 | how a play’s characters will split into factions using only information about the | 1090 | characters are modeled as edge weights in the |
| 806 | state of the sentiment network after Act II. The results of their analysis are varied | 1091 | network. Motivated by the desire to explain such |
| 807 | and do not provide adequate support for the Structural Balance Theory as a benchmark | 1092 | networks in terms of a general sociological model, |
| 808 | for network analysis in Shakespeare’s plays. One reason for that, as the authors | 1093 | Nalisnick and Baird test whether Shakespeare’s |
| 809 | state, is inadequacy of their shallow sentiment analysis methods that cannot detect | 1094 | plays obey the <span style="color:#035151"><i>Structural Balance |
| 810 | such elements of speech as irony and deceit that play a pivotal role in many literary | 1095 | Theory</i></span> by Marvel et al.<a id="fna94" class="fn" href="#fn94" title="Marvel et al. 2011.">[94]</a> that |
| 811 | works. | 1096 | postulates that a friend of a friend is also your |
| 812 | </p> | 1097 | friend. Using the procedure proposed by Marvel et |
| 813 | </div> | 1098 | al. on their Shakespearean sentiment networks, |
| 814 | <div id="subchapter"><a name="hd21"> </a><h3> | 1099 | Nalisnick and Baird test whether they can predict |
| 815 | <div style="position:relative;width:90%;">4.4.2 Character analysis and character relationships</div> | 1100 | how a play’s characters will split into factions |
| 1101 | using only information about the state of the | ||
| 1102 | sentiment network after Act II. The results of | ||
| 1103 | their analysis are varied and do not provide | ||
| 1104 | adequate support for the Structural Balance Theory | ||
| 1105 | as a benchmark for network analysis in | ||
| 1106 | Shakespeare’s plays. One reason for that, as the | ||
| 1107 | authors state, is inadequacy of their shallow | ||
| 1108 | sentiment analysis methods that cannot detect such | ||
| 1109 | elements of speech as irony and deceit that play a | ||
| 1110 | pivotal role in many literary works. | ||
| 1111 | </p> | ||
| 1112 | </div><a name="div25"> </a><div id="subchapter"><a name="hd23"> </a><h3> | ||
| 1113 | <div style="position:relative;width:90%;">4.4.2 Character analysis and character | ||
| 1114 | relationships | ||
| 1115 | </div> | ||
| 816 | </h3> | 1116 | </h3> |
| 817 | <p>Elsner<a id="fna88" class="fn" href="#fn88" title="Elsner 2012, passim; Elsner 2015, passim.">[88]</a> aims at answering the | 1117 | <p id="pid62"><span style="float: right; padding: .2em; color: #999; border: 1px solid #ccc; background-color: #eee; margin-right: -80px;">[<a href="#pid62">62</a>]</span>Elsner<a id="fna95" class="fn" href="#fn95" title="Elsner 2012; Elsner 2015.">[95]</a> aims at answering the question |
| 818 | question of how to represent a plot structure for summarization and generation tools. | 1118 | of how to represent a plot structure for |
| 819 | To that end, Elsner presents a <i>kernel</i> for comparing novelistic | 1119 | summarization and generation tools. To that end, |
| 820 | plots at the level of character interactions and their relationships. Using sentiment | 1120 | Elsner presents a <i>kernel</i> |
| 821 | as one of the characteristics of a character, Elsner demonstrates that the kernel | 1121 | for comparing novelistic plots at the level of |
| 822 | approach leads to meaningful plot representation that can be used for a higher-level | 1122 | character interactions and their relationships. |
| 1123 | Using sentiment as one of the properties of a | ||
| 1124 | character, Elsner demonstrates that the kernel | ||
| 1125 | approach leads to meaningful plot representation | ||
| 1126 | that can be used for a higher-level | ||
| 823 | processing. | 1127 | processing. |
| 824 | </p> | 1128 | </p> |
| 825 | <p>Kim and Klinger<a id="fna89" class="fn" href="#fn89" title="Kim / Klinger 2018, passim.">[89]</a> aim at understanding | 1129 | <p id="pid63"><span style="float: right; padding: .2em; color: #999; border: 1px solid #ccc; background-color: #eee; margin-right: -80px;">[<a href="#pid63">63</a>]</span>Kim and Klinger<a id="fna96" class="fn" href="#fn96" title="Kim / Klinger 2018.">[96]</a> aim at understanding the |
| 826 | the causes of emotions experienced by literary characters. To that end, they | 1130 | causes of emotions experienced by literary |
| 827 | contribute the <a href="http://www.ims.uni-stuttgart.de/data/reman" target="_blank">REMAN | 1131 | characters. To that end, they contribute the <a href="http://www.ims.uni-stuttgart.de/data/reman" target="_blank">REMAN corpus</a><a id="fna97" class="fn" href="#fn97" title="REMAN – Relational Emotion Annotation for Fiction. Corpus 2018.">[97]</a> of literary texts with |
| 828 | corpus</a><a id="fna90" class="fn" href="#fn90" title="REMAN - Relational Emotion Annotation for Fiction. Corpus 2018.">[90]</a> of literary texts with annotations of emotions, | 1132 | annotations of emotions, experiencers, causes and |
| 829 | experiencers, causes and targets of the emotions. The goal of the project is to | 1133 | targets of the emotions. The goal of the project |
| 830 | enable the automatic extraction of emotions and causes of emotions experienced by | 1134 | is to enable the automatic extraction of emotions |
| 831 | the | 1135 | and causes of emotions experienced by the |
| 832 | characters. The authors suggest that the results of coarse-grained emotion | 1136 | characters. The authors suggest that the results |
| 833 | classification in literary text are not readily interpretable as they do not tell | 1137 | of coarse-grained emotion classification in |
| 834 | much about who the experiencer of the emotion is. Indeed, if a text mentions two | 1138 | literary text are not readily interpretable as |
| 835 | characters, one of whom is <i>angry</i> and another one who is <i>scared</i> because of that, text classification models will only | 1139 | they do not tell much about who the experiencer of |
| 836 | tell us that the text is about <i>anger</i> and <i>fear</i>. Hence, a finer-grained approach towards character relationship | 1140 | the emotion is. Indeed, if a text mentions two |
| 837 | extraction is warranted. Kim and Klinger conduct experiments on the annotated dataset | 1141 | characters, one of whom is <i>angry</i> and another one who is <i>scared</i> because of that, text |
| 838 | showing that the fine-grained approach to emotion prediction with long short-term | 1142 | classification models will only tell us that the |
| 839 | memory networks outperforms <span style="color:#035151"><i>bag-of-words models</i></span> (an increase | 1143 | text is about <i>anger</i> and <i>fear</i>. Hence, a finer-grained |
| 840 | in F1 by 12 pp). At the same time, the results of their experiments suggest that | 1144 | approach towards character relationship extraction |
| 841 | joint prediction of emotions and experiencers can be more beneficial than studying | 1145 | is warranted. Kim and Klinger conduct experiments |
| 842 | these categories separately. | 1146 | on the annotated dataset showing that the |
| 843 | </p> | 1147 | fine-grained approach to emotion prediction with |
| 844 | <p>Barth et al.<a id="fna91" class="fn" href="#fn91" title="Barth et al. 2018, passim.">[91]</a> develop the character | 1148 | long short-term memory networks outperforms <span style="color:#035151"><i>bag-of-words models</i></span>. At the same |
| 845 | relation analysis tool <span style="color:#035151"><i>rCAT</i></span> with the goal of visualization and | 1149 | time, the results of their experiments suggest |
| 846 | analysis of character networks in a literary text. The tool implements a distance | 1150 | that joint prediction of emotions and experiencers |
| 847 | parameter (based on token space) for finding pairs of interacting characters. In | 1151 | can be more beneficial than studying these |
| 848 | addition to the general context words that characterize each pair of characters, the | 1152 | categories separately. |
| 849 | tool provides an emotion filter to restrict character relationship analysis to | 1153 | </p> |
| 850 | emotions only. | 1154 | <p id="pid64"><span style="float: right; padding: .2em; color: #999; border: 1px solid #ccc; background-color: #eee; margin-right: -80px;">[<a href="#pid64">64</a>]</span>A tool presented by Jhavar and Mirza<a id="fna98" class="fn" href="#fn98" title="Jhavar / Mirza 2018.">[98]</a> |
| 851 | </p> | 1155 | provides a similar functionality: given an input |
| 852 | <p>A tool presented by Jhavar and Mirza<a id="fna92" class="fn" href="#fn92" title="Jhavar / Mirza 2018, passim.">[92]</a> provides a similar functionality: given an input of two character | 1156 | of two character names from the <i>Harry Potter</i> series, the <a href="https://gate.d5.mpi-inf.mpg.de/emofiel/" target="_blank">EMoFiel</a><a id="fna99" class="fn" href="#fn99" title="EMoFiel: Emotion Mapping of Fictional Relationship 2018.">[99]</a> tool |
| 853 | names from the <i>Harry Potter</i> series, the <a href="https://gate.d5.mpi-inf.mpg.de/emofiel/" target="_blank">EMoFiel</a><a id="fna93" class="fn" href="#fn93" title="EMoFiel: Emotion Mapping of Fictional Relationship 2018.">[93]</a> tool identifies the emotion flow between a | 1157 | identifies the emotion flow between a given |
| 854 | given directed pair of story characters. These emotions are identified using | 1158 | directed pair of story characters. These emotions |
| 855 | categorical<a id="fna94" class="fn" href="#fn94" title="Plutchik 1991, passim.">[94]</a> and continuous<a id="fna95" class="fn" href="#fn95" title="Russell 1980, passim.">[95]</a> emotion models. | 1159 | are identified using categorical<a id="fna100" class="fn" href="#fn100" title="Plutchik 1991.">[100]</a> and |
| 856 | </p> | 1160 | continuous<a id="fna101" class="fn" href="#fn101" title="Russell 1980.">[101]</a> emotion models. |
| 857 | <p>Egloff et al.<a id="fna96" class="fn" href="#fn96" title="Egloff et al. 2018, passim.">[96]</a> present an ongoing | 1161 | </p> |
| 858 | work on the <span style="color:#035151"><i>Ontology of Literary Characters</i></span> (OLC) that allows | 1162 | <p id="pid65"><span style="float: right; padding: .2em; color: #999; border: 1px solid #ccc; background-color: #eee; margin-right: -80px;">[<a href="#pid65">65</a>]</span>Egloff et al.<a id="fna102" class="fn" href="#fn102" title="Egloff et al. 2018.">[102]</a> present an ongoing work on the |
| 859 | us to capture and infer characters’ psychological traits from their linguistic | 1163 | <span style="color:#035151"><i>Ontology of Literary |
| 860 | descriptions. The OLC incorporates the <span style="color:#035151"><i>Ontology of Emotion</i></span><a id="fna97" class="fn" href="#fn97" title="Patti et al. 2015.">[97]</a> that is based on both Plutchik’s and | 1164 | Characters</i></span> (OLC) that allows us to capture |
| 861 | Hourglass’s<a id="fna98" class="fn" href="#fn98" title="Cambria et al. 2012, passim.">[98]</a> models of emotions. | 1165 | and infer characters’ psychological traits from |
| 862 | The ontology encodes 32 emotion concepts. Based on their natural language | 1166 | their linguistic descriptions. The OLC |
| 863 | description, characters are attributed to a psychological profile along the classes | 1167 | incorporates the <span style="color:#035151"><i>Ontology of |
| 864 | of Openness to <i>experience</i>, <i>Conscientiousness</i>, <i>Extraversion</i>, <i>Agreeableness</i>, and <i>Neuroticism</i>. The ontology links | 1168 | Emotion</i></span><a id="fna103" class="fn" href="#fn103" title="Patti et al. 2015.">[103]</a> |
| 865 | each of these profiles to one or more archetypal categories of <i>hero</i>, <i>anti-hero</i>, and <i>villain</i>. | 1169 | that is based on both Plutchik’s and |
| 866 | Egloff et al. argue that, by using the semantic connections of the OLC, it is | 1170 | Hourglass’s<a id="fna104" class="fn" href="#fn104" title="Cambria et al. 2012.">[104]</a> models of emotions. The ontology |
| 867 | possible to infer the characters’ psychological profiles and the role they play in | 1171 | encodes 32 emotion concepts. Based on their |
| 1172 | natural language description, characters are | ||
| 1173 | attributed to a psychological profile along the | ||
| 1174 | classes of <i>Openness</i> to <i>experience</i>, <i>Conscientiousness</i>, <i>Extraversion</i>, <i>Agreeableness</i>, and <i>Neuroticism</i>. The ontology links each of | ||
| 1175 | these profiles to one or more archetypal | ||
| 1176 | categories of <i>hero</i>, <i>anti-hero</i>, and <i>villain</i>. Egloff et al. argue | ||
| 1177 | that, by using the semantic connections of the | ||
| 1178 | OLC, it is possible to infer the characters’ | ||
| 1179 | psychological profiles and the role they play in | ||
| 868 | the plot. | 1180 | the plot. |
| 869 | </p> | 1181 | </p> |
| 870 | <p>Kim and Klinger<a id="fna99" class="fn" href="#fn99" title="Kim / Klinger 2019b, passim.">[99]</a> propose a new task | 1182 | <p id="pid66"><span style="float: right; padding: .2em; color: #999; border: 1px solid #ccc; background-color: #eee; margin-right: -80px;">[<a href="#pid66">66</a>]</span>Kim and Klinger<a id="fna105" class="fn" href="#fn105" title="Kim / Klinger 2019b.">[105]</a> propose the task of emotion |
| 871 | of emotion relationship classification between fictional characters. They argue that | 1183 | relationship classification between fictional |
| 872 | joining character network analysis with sentiment and emotion analysis may contribute | 1184 | characters. They argue that joining character |
| 873 | to a computational understanding of narrative structures, as characters are at the | 1185 | network analysis with sentiment and emotion |
| 874 | center of any plot development. Building a corpus of 19 fan fiction short stories | 1186 | analysis may contribute to a computational |
| 875 | and | 1187 | understanding of narrative structures, as |
| 876 | annotating it with emotions, Kim and Klinger propose several models to classify | 1188 | characters are at the center of any plot |
| 877 | emotion relations of characters. They show that a deep learning architecture with | 1189 | development. Building a corpus of 19 fan fiction |
| 878 | character position indicators is the best for the task of predicting both directed | 1190 | short stories and annotating it with emotions, Kim |
| 879 | and undirected emotion relations in the associated social network graph. As an | 1191 | and Klinger propose several models to classify |
| 880 | extension to this study, Kim and Klinger<a id="fna100" class="fn" href="#fn100" title="Kim / Klinger 2019a, passim.">[100]</a> explore how emotions are expressed between characters in the same | 1192 | emotion relations of characters. They show that a |
| 881 | corpus via various non-verbal communication channels.<a id="fna101" class="fn" href="#fn101" title="Their analysis is based on Van Meel 1995 we mentioned in Section 3.">[101]</a> They find | 1193 | deep learning architecture with character position |
| 882 | that facial expressions are predominantly associated with <i>joy</i> | 1194 | indicators is the best for the task of predicting |
| 883 | while gestures and body postures are more likely to occur with <i>trust</i>. | 1195 | both directed and undirected emotion relations in |
| 884 | </p> | 1196 | the associated social network graph. As an |
| 885 | <p>Finally, a small body of work focuses on mathematical modeling of character | 1197 | extension to this study, Kim and Klinger<a id="fna106" class="fn" href="#fn106" title="Kim / Klinger 2019a.">[106]</a> |
| 886 | relationships. Rinaldi et al.<a id="fna102" class="fn" href="#fn102" title="Rinaldi et al. 2013, passim.">[102]</a> | 1198 | explore how emotions are expressed between |
| 887 | contribute a model that describes the love story between the Beauty and the Beast | 1199 | characters in the same corpus via various |
| 888 | through ordinary differential equations. Zhuravlev et al.<a id="fna103" class="fn" href="#fn103" title="Zhuravlev et al. 2014, passim.">[103]</a> introduce a distance function to model the | 1200 | non-verbal communication channels.<a id="fna107" class="fn" href="#fn107" title="Their analysis is based on Van Meel 1995 we mentioned in section 3.">[107]</a> They find |
| 889 | relationship between the protagonist and other characters in two masochistic short | 1201 | that facial expressions are predominantly |
| 890 | novels by Ivan Turgenev and Sacher-Masoch. Borrowing some instruments from the | 1202 | associated with <i>joy</i> while |
| 891 | literary criticism and using ordinary differential equations, Zhuravlev et al. are | 1203 | gestures and body postures are more likely to |
| 892 | able to reproduce the temporal and spatial dynamics of the love plot in the two | 1204 | occur with <i>trust</i>. |
| 893 | novellas more precisely than it had been done in previous research. Jafari et | 1205 | </p> |
| 894 | al.<a id="fna104" class="fn" href="#fn104" title="Jafari et al. 2016, passim.">[104]</a> present a dynamic model | 1206 | <p id="pid67"><span style="float: right; padding: .2em; color: #999; border: 1px solid #ccc; background-color: #eee; margin-right: -80px;">[<a href="#pid67">67</a>]</span>Finally, a small body of work focuses on |
| 895 | describing the development of character relationships based on differential | 1207 | mathematical modeling of character relationships. |
| 896 | equations. The proposed model is enriched with complex variables that can represent | 1208 | Rinaldi et al.<a id="fna108" class="fn" href="#fn108" title="Rinaldi et al. 2013.">[108]</a> contribute a model that |
| 897 | complex emotions such as coexisting <i>love</i> and <i>hate</i>. | 1209 | describes the love story between the <i>Beauty and the Beast</i> through ordinary differential equations. |
| 898 | </p> | 1210 | Zhuravlev et al.<a id="fna109" class="fn" href="#fn109" title="Zhuravlev et al. 2014.">[109]</a> introduce a distance function |
| 899 | </div> | 1211 | to model the relationship between the protagonist |
| 900 | </div> | 1212 | and other characters in two masochistic short |
| 901 | <div id="subchapter"><a name="hd22"> </a><h3> | 1213 | novels by Ivan Turgenev and Sacher-Masoch. |
| 1214 | Borrowing some instruments from the literary | ||
| 1215 | criticism and using ordinary differential | ||
| 1216 | equations, Zhuravlev et al. are able to reproduce | ||
| 1217 | the temporal and spatial dynamics of the love plot | ||
| 1218 | in the two novellas more precisely than it had | ||
| 1219 | been done in previous research. Jafari et al.<a id="fna110" class="fn" href="#fn110" title="Jafari et al. 2016.">[110]</a> | ||
| 1220 | present a dynamic model describing the development | ||
| 1221 | of character relationships based on differential | ||
| 1222 | equations. The proposed model is enriched with | ||
| 1223 | complex variables that can represent complex | ||
| 1224 | emotions such as coexisting <i>love</i> and <i>hate</i>. | ||
| 1225 | </p> | ||
| 1226 | </div> | ||
| 1227 | </div><a name="div26"> </a><div id="subchapter"><a name="hd24"> </a><h3> | ||
| 902 | <div style="position:relative;width:90%;">4.5 Other Types of Emotion Analysis</div> | 1228 | <div style="position:relative;width:90%;">4.5 Other Types of Emotion Analysis</div> |
| 903 | </h3> | 1229 | </h3> |
| 904 | <p>We have seen that sentiment analysis as applied to literature can be used for a | 1230 | <p id="pid68"><span style="float: right; padding: .2em; color: #999; border: 1px solid #ccc; background-color: #eee; margin-right: -80px;">[<a href="#pid68">68</a>]</span>We have seen that sentiment analysis as applied |
| 905 | number of downstream tasks, such as classification of texts based on the emotions | 1231 | to literature can be used for a number of |
| 906 | they convey, genre classification based on emotions, and sentiment analysis in the | 1232 | downstream tasks, such as classification of texts |
| 907 | historical domain. However, the application of sentiment analysis is not limited to | 1233 | based on the emotions they convey, genre |
| 908 | these tasks. In this concluding part of the survey, we review some papers that do | 1234 | classification based on emotions, and sentiment |
| 909 | not | 1235 | analysis in the historical domain. However, the |
| 910 | formulate their approach to sentiment analysis as a downstream task. Often, the goal | 1236 | application of sentiment analysis is not limited |
| 911 | of these works is to understand how sentiments and emotions are represented in | 1237 | to these tasks. In this concluding part of the |
| 912 | literary texts in general, and how sentiment or emotion content varies across | 1238 | survey, we review some papers that do not |
| 913 | specific documents or a collection of them with time, where time can be either | 1239 | formulate their approach to sentiment analysis as |
| 914 | relative to the text in question (from beginning to end) or to the historical changes | 1240 | a downstream task. Often, the goal of these works |
| 915 | in language (from past to present). Such information is valuable for gaining a deeper | 1241 | is to understand how sentiments and emotions are |
| 916 | insight into how sentiments and emotions change over time, allowing us to bring | 1242 | represented in literary texts in general, and how |
| 917 | forward new theories or shed more light onto existing literary or sociological | 1243 | sentiment or emotion content varies across |
| 918 | theories. | 1244 | specific documents or a collection of them with |
| 919 | </p> | 1245 | time, where time can be either relative to the |
| 920 | <div id="subchapter"><a name="hd23"> </a><h3> | 1246 | text in question (from beginning to end) or to the |
| 921 | <div style="position:relative;width:90%;">4.5.1 Emotion flow analysis and visualization</div> | 1247 | historical changes in language (from past to |
| 1248 | present). Such information is valuable for gaining | ||
| 1249 | a deeper insight into how sentiments and emotions | ||
| 1250 | change over time, allowing us to bring forward new | ||
| 1251 | theories or shed more light onto existing literary | ||
| 1252 | or sociological theories. | ||
| 1253 | </p><a name="div27"> </a><div id="subchapter"><a name="hd25"> </a><h3> | ||
| 1254 | <div style="position:relative;width:90%;">4.5.1 Emotion flow analysis and | ||
| 1255 | visualization | ||
| 1256 | </div> | ||
| 922 | </h3> | 1257 | </h3> |
| 923 | <p>A set of authors aimed to visualize the change of emotion content through texts or | 1258 | <p id="pid69"><span style="float: right; padding: .2em; color: #999; border: 1px solid #ccc; background-color: #eee; margin-right: -80px;">[<a href="#pid69">69</a>]</span>A set of authors aimed to visualize the change |
| 924 | across time. One of the earliest works in this direction is a paper by Anderson and | 1259 | of emotion content through texts or across time. |
| 925 | McMaster<a id="fna105" class="fn" href="#fn105" title="Anderson / McMaster 1986, passim.">[105]</a> that starts from | 1260 | One of the earliest works in this direction is a |
| 926 | the premise that reading enjoyment stems from the affective tones of a text. These | 1261 | paper by Anderson and McMaster<a id="fna111" class="fn" href="#fn111" title="Anderson / McMaster 1986.">[111]</a> |
| 927 | affective tones create a conflict that can rise to a climax through a series of | 1262 | that starts from the premise that reading |
| 928 | crises, which is necessary for a work of fiction to be attractive to the reader. | 1263 | enjoyment stems from the affective tones of a |
| 929 | Using a list of 1,000 of the most common English words annotated with valence, | 1264 | text. These affective tones create a conflict that |
| 930 | arousal, and dominance ratings,<a id="fna106" class="fn" href="#fn106" title="Heise 1965, passim.">[106]</a> they | 1265 | can rise to a climax through a series of crises, |
| 931 | calculate the conflict score by taking the mean of the ratings for each word in a | 1266 | which is necessary for a work of fiction to be |
| 932 | text passage. The more negative the score is, the higher the conflict is, and vice | 1267 | attractive to the reader. Using a list of 1,000 of |
| 933 | versa. Additionally, they plot conflict scores for each consecutive 100 words of a | 1268 | the most common English words annotated with |
| 934 | test story and provide qualitative analysis of the peaks. They argue that a reader | 1269 | valence, arousal, and dominance ratings,<a id="fna112" class="fn" href="#fn112" title="Heise 1965.">[112]</a> they calculate |
| 935 | who has access to the text would be able to find correlation between events in the | 1270 | the conflict score by taking the mean of the |
| 936 | story and peaks on the graph. However, the authors still stress that such | 1271 | ratings for each word in a text passage. The more |
| 937 | interpretation remains dependent upon the judgement of the reader. Further, other | 1272 | negative the score is, the higher the conflict is, |
| 938 | contributions by the authors are based on the same premises.<a id="fna107" class="fn" href="#fn107" title="Anderson / McMaster 1982; Anderson / McMaster 1993.">[107]</a></p> | 1273 | and vice versa. Additionally, they plot conflict |
| 939 | <p>Alm and Sproat<a id="fna108" class="fn" href="#fn108" title="Alm / Sproat 2005, passim.">[108]</a> present the results of | 1274 | scores for each consecutive 100 words of a test |
| 940 | the emotion annotation task of 22 tales by the Grimm brothers and evaluate patterns | 1275 | story and provide qualitative analysis of the |
| 941 | of emotional story development. They split emotions into <i>positive</i> and <i>negative</i> categories and divide each | 1276 | peaks. They argue that a reader who has access to |
| 942 | story into five parts from which aggregate frequency counts of combined emotion | 1277 | the text would be able to find correlation between |
| 943 | categories are computed. The resulting numbers are plotted on a graph that shows a | 1278 | events in the story and peaks on the graph. |
| 944 | wave-shaped pattern. From this graph, Alm and Sproat argue, one can see that the | 1279 | However, the authors still stress that such |
| 945 | first part of the fairy tales is the least emotional, which is probably due to scene | 1280 | interpretation remains dependent upon the |
| 946 | setting, while the last part shows an increase in positive emotions, which may | 1281 | judgement of the reader. Further, other |
| 947 | signify the happy ending. | 1282 | contributions by the authors are based on the same |
| 948 | </p> | 1283 | premises.<a id="fna113" class="fn" href="#fn113" title="Anderson / McMaster 1982; Anderson / McMaster 1993.">[113]</a></p> |
| 949 | <p>Two other studies by Mohammad<a id="fna109" class="fn" href="#fn109" title="Mohammad 2011, passim; Mohammad 2012, passim.">[109]</a> focus on differences in emotion word density as well as emotional | 1284 | <p id="pid70"><span style="float: right; padding: .2em; color: #999; border: 1px solid #ccc; background-color: #eee; margin-right: -80px;">[<a href="#pid70">70</a>]</span>Alm and Sproat<a id="fna114" class="fn" href="#fn114" title="Alm / Sproat 2005.">[114]</a> present the results of the |
| 950 | trajectories between books of different genres. Emotion word density is defined as | 1285 | emotion annotation task of 22 tales by the Grimm |
| 951 | a | 1286 | brothers and evaluate patterns of emotional story |
| 952 | number of times a reader will encounter an emotion word on reading every <i>X words</i>. In addition, each text is assigned several emotion | 1287 | development. They split emotions into <i>positive</i> and <i>negative</i> categories and divide each story |
| 953 | scores for each emotion that are calculated as a ratio of words associated with one | 1288 | into five parts from which aggregate frequency |
| 954 | emotion to the total number of emotion words occurring in a text. Both metrics use | 1289 | counts of combined emotion categories are |
| 955 | the <span style="color:#035151"><i>NRC Affective Lexicon</i></span> to find occurrences of emotion | 1290 | computed. The resulting numbers are plotted on a |
| 956 | words. They find that fairy tales have significantly higher <i>anticipation</i>, <i>disgust</i>, <i>joy</i> and | 1291 | graph that shows a wave-shaped pattern. From this |
| 957 | <i>surprise</i> word densities, but lower <i>trust</i> word densities when compared to novels. | 1292 | graph, Alm and Sproat argue, one can see that the |
| 958 | </p> | 1293 | first part of the fairy tales is the least |
| 959 | <p>A work by Klinger et al.<a id="fna110" class="fn" href="#fn110" title="Klinger et al. 2016, passim.">[110]</a> is a case | 1294 | emotional, which is probably due to scene setting, |
| 960 | study in an automatic emotion analysis of Kafka’s <i>Amerika</i> and <i>Das Schloss</i>. The goal of the work is to analyze the development of emotions in both texts | 1295 | while the last part shows an increase in positive |
| 961 | as well as to provide a character-oriented emotion analysis that would reveal | 1296 | emotions, which may signify the happy ending. |
| 962 | specific character traits in both texts. To that end, Klinger et al. develop German | 1297 | </p> |
| 963 | dictionaries of words associated with Ekman’s fundamental emotions plus contempt and | 1298 | <p id="pid71"><span style="float: right; padding: .2em; color: #999; border: 1px solid #ccc; background-color: #eee; margin-right: -80px;">[<a href="#pid71">71</a>]</span>Two other studies by Mohammad<a id="fna115" class="fn" href="#fn115" title="Mohammad 2011; Mohammad 2012.">[115]</a> focus on differences in emotion word |
| 964 | apply them to both texts in question to automatically detect emotion words. The | 1299 | density as well as emotional trajectories between |
| 965 | results of their analysis for <i>Das Schloss</i> show a striking increase of <i>surprise</i> towards the end | 1300 | books of different genres. Emotion word density is |
| 966 | and a peak of <i>fear</i> shortly after start of chapter 3. In the | 1301 | defined as a number of times a reader will |
| 967 | case of <i>Amerika</i>, the analysis shows that there is a decrease in <i>enjoyment</i> after a peak in chapter 4. | 1302 | encounter an emotion word on reading every <i>X words</i>. In addition, each text |
| 968 | </p> | 1303 | is assigned several emotion scores for each |
| 969 | <p>Yet another work that tracks the flow of emotions in a collection of texts is | 1304 | emotion that are calculated as a ratio of words |
| 970 | presented by Kim et al.<a id="fna111" class="fn" href="#fn111" title="Kim et al. 2017b, passim.">[111]</a> The authors | 1305 | associated with one emotion to the total number of |
| 971 | hypothesize that literary genres can be linked to the development of emotions over | 1306 | emotion words occurring in a text. Both metrics |
| 972 | the course of text. To test this, they collect more than 2,000 books from five genres | 1307 | use the <span style="color:#035151"><i>NRC Affective |
| 973 | (<i>adventure</i>, <i>science fiction</i>, <i>mystery</i>, <i>humor</i> and <i>romance</i>) from Project Gutenberg and identify prototypical emotion shapes for | 1308 | Lexicon</i></span> to find occurrences of emotion |
| 974 | each genre. Each novel in the corpus is split into five consecutive equally-sized | 1309 | words. They find that fairy tales have |
| 975 | segments (following the five-act theory of dramatic acts).<a id="fna112" class="fn" href="#fn112" title="Freytag 1863, passim.">[112]</a> All five genres show close correspondence with regard to <i>sadness</i>, <i>anger</i>, <i>fear</i> and <i>disgust</i>, i.e., a consistent increase of | 1310 | significantly higher <i>anticipation</i>, <i>disgust</i>, <i>joy</i> and <i>surprise</i> word densities, but |
| 976 | these emotions from Act 1 to Act 5, which may correspond to an entertaining | 1311 | lower <i>trust</i> word densities |
| 977 | narrative. <i>Mystery</i> and <i>science fiction</i> | 1312 | when compared to novels. |
| 978 | books show increase in <i>anger</i> towards the end, and <i>joy</i> shows an inverse decreasing pattern from Act 1 to Act 2, | 1313 | </p> |
| 979 | with the exception of <i>humor</i>. | 1314 | <p id="pid72"><span style="float: right; padding: .2em; color: #999; border: 1px solid #ccc; background-color: #eee; margin-right: -80px;">[<a href="#pid72">72</a>]</span>A work by Klinger et al.<a id="fna116" class="fn" href="#fn116" title="Klinger et al. 2016.">[116]</a> is a case study in an |
| 980 | </p> | 1315 | automatic emotion analysis of Kafka’s <i>Amerika</i> and <i>Das Schloss</i>. The goal of the work is to analyze the |
| 981 | <p>The work by Kakkonen and Galic Kakkonen<a id="fna113" class="fn" href="#fn113" title="Kakkonen / Galic Kakkonen 2011, passim.">[113]</a> aims at supporting the literary analysis of <i>Gothic</i> texts at the sentiment level. The authors introduce a | 1316 | development of emotions in both texts as well as |
| 982 | system called <span style="color:#035151"><i>SentiProfiler</i></span> that generates visual | 1317 | to provide a character-oriented emotion analysis |
| 983 | representations of affective content in such texts and outlines similarities and | 1318 | that would reveal specific character traits in |
| 984 | differences between them, however, without considering the temporal dimension. The | 1319 | both texts. To that end, Klinger et al. develop |
| 985 | <span style="color:#035151"><i>SentiProfiler</i></span> uses <span style="color:#035151"><i>WordNet-Affect</i></span> to | 1320 | German dictionaries of words associated with |
| 986 | derive a list of emotion-bearing words that will be used for analysis. The resulting | 1321 | Ekman’s fundamental emotions plus contempt and |
| 987 | sentiment profiles for the books are used to visualize the presence of sentiment in | 1322 | apply them to both texts in question to |
| 988 | a | 1323 | automatically detect emotion words. The results of |
| 989 | particular document and to compare two different texts. | 1324 | their analysis for <i>Das Schloss</i> show a striking increase of <i>surprise</i> towards the end and a |
| 990 | </p> | 1325 | peak of <i>fear</i> shortly after |
| 991 | </div> | 1326 | start of chapter 3. In the case of <i>Amerika</i>, the analysis shows that there is a |
| 992 | <div id="subchapter"><a name="hd24"> </a><h3> | 1327 | decrease in <i>enjoyment</i> after |
| 1328 | a peak in chapter 4. | ||
| 1329 | </p> | ||
| 1330 | <p id="pid73"><span style="float: right; padding: .2em; color: #999; border: 1px solid #ccc; background-color: #eee; margin-right: -80px;">[<a href="#pid73">73</a>]</span>A similar study by Schmidt and Burghardt<a id="fna117" class="fn" href="#fn117" title="Schmidt / Burghardt 2018.">[117]</a> | ||
| 1331 | also works on German text – but focuses on the | ||
| 1332 | mostly neglected domain of theater plays, more | ||
| 1333 | concretely the plays by Lessing. They perform an | ||
| 1334 | annotation study and subsequently analyze | ||
| 1335 | different established emotion lexicons to recover | ||
| 1336 | the emotion automatically. The configuration of | ||
| 1337 | the best performing system shows the highest | ||
| 1338 | accuracy of 0.7, while a majority baseline obtains | ||
| 1339 | 0.695. | ||
| 1340 | </p> | ||
| 1341 | <p id="pid74"><span style="float: right; padding: .2em; color: #999; border: 1px solid #ccc; background-color: #eee; margin-right: -80px;">[<a href="#pid74">74</a>]</span>Yet another work that tracks the flow of | ||
| 1342 | emotions in a collection of texts is presented by | ||
| 1343 | Kim et al.<a id="fna118" class="fn" href="#fn118" title="Kim et al. 2017b.">[118]</a> The authors hypothesize that | ||
| 1344 | literary genres can be linked to the development | ||
| 1345 | of emotions over the course of text. To test this, | ||
| 1346 | they collect more than 2,000 books from five | ||
| 1347 | genres (<i>adventure</i>, <i>science fiction</i>, <i>mystery</i>, <i>humor</i> and <i>romance</i>) | ||
| 1348 | from Project Gutenberg and identify prototypical | ||
| 1349 | emotion shapes for each genre. Each novel in the | ||
| 1350 | corpus is split into five consecutive | ||
| 1351 | equally-sized segments (following the five-act | ||
| 1352 | theory of dramatic acts).<a id="fna119" class="fn" href="#fn119" title="Freytag 1863.">[119]</a> All five genres show close | ||
| 1353 | correspondence with regard to <i>sadness</i>, <i>anger</i>, <i>fear</i> and <i>disgust</i>, i.e., a consistent increase of | ||
| 1354 | these emotions from Act 1 to Act 5, which may | ||
| 1355 | correspond to an entertaining narrative. <i>Mystery</i> and <i>science fiction</i> books show increase in <i>anger</i> towards the end, and <i>joy</i> shows an inverse decreasing | ||
| 1356 | pattern from Act 1 to Act 2, with the exception of | ||
| 1357 | <i>humor</i>. | ||
| 1358 | </p> | ||
| 1359 | <p id="pid75"><span style="float: right; padding: .2em; color: #999; border: 1px solid #ccc; background-color: #eee; margin-right: -80px;">[<a href="#pid75">75</a>]</span>The work by Kakkonen and Galic Kakkonen<a id="fna120" class="fn" href="#fn120" title="Kakkonen / Galic Kakkonen 2011.">[120]</a> aims at supporting the literary | ||
| 1360 | analysis of <i>Gothic</i> texts at | ||
| 1361 | the sentiment level. The authors introduce a | ||
| 1362 | system called <span style="color:#035151"><i>SentiProfiler</i></span> | ||
| 1363 | that generates visual representations of affective | ||
| 1364 | content in such texts and outlines similarities | ||
| 1365 | and differences between them, however, without | ||
| 1366 | considering the temporal dimension. The <span style="color:#035151"><i>SentiProfiler</i></span> uses <span style="color:#035151"><i>WordNet-Affect</i></span> to derive a list | ||
| 1367 | of emotion-bearing words that will be used for | ||
| 1368 | analysis. The resulting sentiment profiles for the | ||
| 1369 | books are used to visualize the presence of | ||
| 1370 | sentiment in a particular document and to compare | ||
| 1371 | two different texts. | ||
| 1372 | </p> | ||
| 1373 | </div><a name="div28"> </a><div id="subchapter"><a name="hd26"> </a><h3> | ||
| 993 | <div style="position:relative;width:90%;">4.5.2 Miscellaneous</div> | 1374 | <div style="position:relative;width:90%;">4.5.2 Miscellaneous</div> |
| 994 | </h3> | 1375 | </h3> |
| 995 | <p>In this section, we review studies that are different in goals and research questions | 1376 | <p id="pid76"><span style="float: right; padding: .2em; color: #999; border: 1px solid #ccc; background-color: #eee; margin-right: -80px;">[<a href="#pid76">76</a>]</span>In this section, we review studies that are |
| 996 | from the papers presented in previous sections and do not constitute a category on | 1377 | different in goals and research questions from the |
| 997 | their own. | 1378 | papers presented in previous sections and do not |
| 998 | </p> | 1379 | constitute a category on their own. |
| 999 | <p>Koolen<a id="fna114" class="fn" href="#fn114" title="Koolen 2018, passim.">[114]</a> claims that there is a bias among | 1380 | </p> |
| 1000 | readers that put works by female authors on par with »women’s books«, which, as | 1381 | <p id="pid77"><span style="float: right; padding: .2em; color: #999; border: 1px solid #ccc; background-color: #eee; margin-right: -80px;">[<a href="#pid77">77</a>]</span>Koolen<a id="fna121" class="fn" href="#fn121" title="Koolen 2018.">[121]</a> claims that there is a bias among |
| 1001 | stated by the author, tend to be perceived as of lower literary quality. She | 1382 | readers that put works by female authors on par |
| 1002 | investigates how much »women’s books« (here, <i>romantic</i> novels | 1383 | with »women’s books«, which, as stated by the |
| 1003 | written by women) differ from novels perceived as literary (female and male-authored | 1384 | author, tend to be perceived as of lower literary |
| 1004 | literary fiction). The corpus used in the study is a collection of European and | 1385 | quality. She investigates how much »women’s books« |
| 1005 | North-American novels translated into Dutch. Koolen uses a Dutch version of the <i>Linguistic Inquiry</i> and <span style="color:#035151"><i>Word Count</i></span>,<a id="fna115" class="fn" href="#fn115" title="Boot et al. 2017.">[115]</a> a dictionary that contains content and sentiment-related categories | 1386 | (here, <i>romantic</i> novels |
| 1006 | of words to count the number of words from different categories in each type of | 1387 | written by women) differ from novels perceived as |
| 1007 | fiction. Her analysis shows that romantic novels contain more positive emotions and | 1388 | literary (female and male-authored literary |
| 1008 | words pertaining to friendship than in literary fiction. However, female-authored | 1389 | fiction). The corpus used in the study is a |
| 1009 | literary novels and male-authored ones do not significantly differ on any category. | 1390 | collection of European and North-American novels |
| 1010 | 1391 | translated into Dutch. Koolen uses a Dutch version | |
| 1011 | </p> | 1392 | of the <i>Linguistic Inquiry</i> and <span style="color:#035151"><i>Word |
| 1012 | <p>Kraicer and Piper<a id="fna116" class="fn" href="#fn116" title="Kraicer / Piper 2019, passim.">[116]</a> explore the | 1393 | Count</i></span>,<a id="fna122" class="fn" href="#fn122" title="Boot et al. 2017.">[122]</a> a dictionary that contains content |
| 1013 | women’s place within contemporary fiction starting from the premise that there is | 1394 | and sentiment-related categories of words to count |
| 1014 | a | 1395 | the number of words from different categories in |
| 1015 | near ubiquitous underrepresentation and decentralization of women. As a part of their | 1396 | each type of fiction. Her analysis shows that |
| 1016 | analysis, Kraicer and Piper use sentiment scores to look at social balance and | 1397 | romantic novels contain more positive emotions and |
| 1017 | »antagonism«, i.e., how different gender pairings influence positive and negative | 1398 | words pertaining to friendship than in literary |
| 1018 | language surrounding the co-occurrence of characters (using the sentiment dictionary | 1399 | fiction. However, female-authored literary novels |
| 1019 | presented by Liu<a id="fna117" class="fn" href="#fn117" title="Liu et al. 2010, passim.">[117]</a> to calculate a | 1400 | and male-authored ones do not significantly differ |
| 1020 | sentiment score for a character pair). Having analyzed a set of 26,450 characters | 1401 | on any category. |
| 1021 | from 1,333 novels published between 2001 and 2015, the authors find that sentiment | 1402 | </p> |
| 1022 | scores give little indication that the character’s gender has an effect on the state | 1403 | <p id="pid78"><span style="float: right; padding: .2em; color: #999; border: 1px solid #ccc; background-color: #eee; margin-right: -80px;">[<a href="#pid78">78</a>]</span>Kraicer and Piper<a id="fna123" class="fn" href="#fn123" title="Kraicer / Piper 2019.">[123]</a> explore the women’s place |
| 1023 | of social balance. | 1404 | within contemporary fiction starting from the |
| 1024 | </p> | 1405 | premise that there is a near ubiquitous |
| 1025 | <p>Morin and Acerbi<a id="fna118" class="fn" href="#fn118" title="Morin / Acerbi 2017, passim.">[118]</a> focus on | 1406 | underrepresentation and decentralization of women. |
| 1026 | larger-scale data spanning a hundred thousand of books. The goal of their study is | 1407 | As a part of their analysis, Kraicer and Piper use |
| 1027 | to | 1408 | sentiment scores to look at social balance and |
| 1028 | understand how emotionality of written texts changed throughout the centuries. Having | 1409 | »antagonism«, i.e., how different gender pairings |
| 1029 | collected 307,527 books written between 1900 and 2000 from the <a href="http://storage.googleapis.com/books/ngrams/books/datasetsv2.html" target="_blank">Google Books | 1410 | influence positive and negative language |
| 1030 | corpus</a><a id="fna119" class="fn" href="#fn119" title="Google Books Ngram Viewer 2012.">[119]</a> they collect, for each | 1411 | surrounding the co-occurrence of characters (using |
| 1031 | year, the total number of case-insensitive occurrences of emotion terms that are | 1412 | the sentiment dictionary presented by Liu<a id="fna124" class="fn" href="#fn124" title="Liu et al. 2010.">[124]</a> to |
| 1032 | found under positive and negative taxonomies of <span style="color:#035151"><i>LIWC</i></span> | 1413 | calculate a sentiment score for a character pair). |
| 1033 | dictionary.<a id="fna120" class="fn" href="#fn120" title="Pennebaker et al. 2007.">[120]</a> The main findings | 1414 | Having analyzed a set of 26,450 characters from |
| 1034 | of their research show that emotionality (both <i>positive</i> and | 1415 | 1,333 novels published between 2001 and 2015, the |
| 1035 | <i>negative</i> emotions) declines with time, and this decline is | 1416 | authors find that sentiment scores give little |
| 1036 | driven by the decrease in usage of positive vocabulary. Morin and Acerbi remind us | 1417 | indication that the character’s gender has an |
| 1037 | that the <i>Romantic</i> period was dominated by emotionality in | 1418 | effect on the state of social balance. |
| 1038 | writing, which could be the effect of a group of writers who wrote above the mean. | 1419 | </p> |
| 1039 | If | 1420 | <p id="pid79"><span style="float: right; padding: .2em; color: #999; border: 1px solid #ccc; background-color: #eee; margin-right: -80px;">[<a href="#pid79">79</a>]</span>Morin and Acerbi<a id="fna125" class="fn" href="#fn125" title="Morin / Acerbi 2017.">[125]</a> focus on larger-scale data |
| 1040 | one assumes that each new writer tends to copy the emotional style of their | 1421 | spanning a hundred thousand of books. The goal of |
| 1041 | predecessors, then writers at one point of time are disproportionally influenced by | 1422 | their study is to understand how emotionality of |
| 1042 | this group of above-the-mean writers. However, this trend does not last forever and, | 1423 | written texts changed throughout the centuries. |
| 1043 | sooner or later, the trend reverts to the mean, as each writer reverts to a normal | 1424 | Having collected 307,527 books written between |
| 1044 | level of emotionality. | 1425 | 1900 and 2000 from the <a href="http://storage.googleapis.com/books/ngrams/books/datasetsv2.html" target="_blank">Google Books corpus</a><a id="fna126" class="fn" href="#fn126" title="Google Books Ngram Viewer 2012.">[126]</a> they |
| 1045 | </p> | 1426 | collect, for each year, the total number of |
| 1046 | <p>An earlier work<a id="fna121" class="fn" href="#fn121" title="Bentley et al. 2014, passim.">[121]</a> written in | 1427 | case-insensitive occurrences of emotion terms that |
| 1047 | collaboration with <span style="color:#035151"><i>Acerbi</i></span> provides a somewhat different | 1428 | are found under positive and negative taxonomies |
| 1048 | approach and interpretation of the problem of the decline in positive vocabulary in | 1429 | of <span style="color:#035151"><i>LIWC</i></span> dictionary.<a id="fna127" class="fn" href="#fn127" title="Pennebaker et al. 2007.">[127]</a> |
| 1049 | English books of the twentieth century. Using the same dataset and lexical resources | 1430 | The main findings of their research show that |
| 1050 | (plus <span style="color:#035151"><i>WordNet-Affect</i></span>) Bentley et al. find a strong correlation | 1431 | emotionality (both <i>positive</i> |
| 1051 | between expressed negative emotions and the <span style="color:#035151"><i>U.S. economic misery | 1432 | and <i>negative</i> emotions) |
| 1052 | index</i></span>, which is especially strong for the books written during and after | 1433 | declines with time, and this decline is driven by |
| 1053 | the World War I (1918), the Great Depression (1935), and the energy crisis (1975). | 1434 | the decrease in usage of positive vocabulary. |
| 1054 | However, in the present study,<a id="fna122" class="fn" href="#fn122" title="Morin / Acerbi 2017, passim.">[122]</a> the | 1435 | Morin and Acerbi remind us that the <i>Romantic</i> period was dominated |
| 1055 | authors argue that the extent to which positive emotionality correlates with | 1436 | by emotionality in writing, which could be the |
| 1056 | subjective well-being is a debatable issue. Morin and Acerbi provide more possible | 1437 | effect of a group of writers who wrote above the |
| 1057 | reasons for this effect as well as detailed statistical analysis of the data, so we | 1438 | mean. If one assumes that each new writer tends to |
| 1058 | refer the reader to the original paper for more information. | 1439 | copy the emotional style of their predecessors, |
| 1440 | then writers at one point of time are | ||
| 1441 | disproportionally influenced by this group of | ||
| 1442 | above-the-mean writers. However, this trend does | ||
| 1443 | not last forever and, sooner or later, the trend | ||
| 1444 | reverts to the mean, as each writer reverts to a | ||
| 1445 | normal level of emotionality. | ||
| 1446 | </p> | ||
| 1447 | <p id="pid80"><span style="float: right; padding: .2em; color: #999; border: 1px solid #ccc; background-color: #eee; margin-right: -80px;">[<a href="#pid80">80</a>]</span>An earlier work<a id="fna128" class="fn" href="#fn128" title="Bentley et al. 2014.">[128]</a> written in collaboration with | ||
| 1448 | Acerbi provides a somewhat different approach and | ||
| 1449 | interpretation of the problem of the decline in | ||
| 1450 | positive vocabulary in English books of the | ||
| 1451 | twentieth century. Using the same dataset and | ||
| 1452 | lexical resources (plus <span style="color:#035151"><i>WordNet-Affect</i></span>) Bentley et al. find a | ||
| 1453 | strong correlation between expressed negative | ||
| 1454 | emotions and the <span style="color:#035151"><i>U.S. economic | ||
| 1455 | misery index</i></span>, which is especially strong | ||
| 1456 | for the books written during and after the World | ||
| 1457 | War I, the Great Depression, and the energy crisis | ||
| 1458 | in the 1970s. However, in the present study,<a id="fna129" class="fn" href="#fn129" title="Morin / Acerbi 2017.">[129]</a> the | ||
| 1459 | authors argue that the extent to which positive | ||
| 1460 | emotionality correlates with subjective well-being | ||
| 1461 | is a debatable issue. Morin and Acerbi provide | ||
| 1462 | more possible reasons for this effect as well as | ||
| 1463 | detailed statistical analysis of the data, so we | ||
| 1464 | refer the reader to the original paper for more | ||
| 1465 | information. | ||
| 1059 | </p> | 1466 | </p> |
| 1060 | <div class="medium"> | 1467 | <div class="medium"> |
| 1061 | <div class="field-item even" rel="og:image rdfs:seeAlso" resource="../medium1"><a href="http://www.zfdg.de/sites/default/files/medien/emotion_analysis_2019_003.png" title="Tab. 1: Summary of characteristics of methods used in the papers reviewed in this survey. Download as PDF. [Kim / Klinger | 1468 | <div class="field-item even" rel="og:image rdfs:seeAlso" resource="../medium1"><a href="http://www.zfdg.de/sites/default/files/medien/emotion_analysis_2019_003.png" title="Tab. 1: Summary of characteristics of methods used in the papers reviewed in this survey. Download as PDF. [Kim / Klinger 2021]" rel="gallery-node" class="colorbox"><img style="max-height:450px!important" class="artikel" alt="Tab. 1: Summary of characteristics of
 methods used in the papers reviewed in this survey. Download as PDF.
 [Kim / Klinger 2021]" id="emotion_analysis_2019_003" src="http://www.zfdg.de/sites/default/files/styles/medium_in_artikel/emotion_analysis_2019_003.png"></a></div> |
| 1062 | <div class="img_desc"><a href="#abb3">Tab. 1</a>: Summary of characteristics of | 1469 | <div class="img_desc"><a href="#abb3">Tab. 1</a>: Summary of characteristics of |
| 1063 | in this survey. Download as <a href=" | 1470 | methods used in the papers reviewed in this survey. Download as <a href="https://www.zfdg.de/files/table_zfdg_klinger.pdf">PDF</a>. |
| 1064 | </div> | 1471 | [Kim / Klinger 2021]<a href="#emotion_analysis_2019_003"></a></div> |
| 1065 | </div> | 1472 | </div> |
| 1066 | </div> | 1473 | </div> |
| 1067 | </div> | 1474 | </div> |
| 1068 | <div id="chapter"><a name=" | 1475 | </div><a name="div29"> </a><div id="chapter"><a name="hd27"> </a><h2> |
| 1069 | <div style="position:relative;width:90%;">5 Discussion and Conclusion</div> | 1476 | <div style="position:relative;width:90%;">5 Discussion and Conclusion</div> |
| 1070 | </h2> | 1477 | </h2> |
| 1071 | <p>We have shown throughout this survey that there is a growing interest in sentiment | 1478 | <p id="pid81"><span style="float: right; padding: .2em; color: #999; border: 1px solid #ccc; background-color: #eee; margin-right: -80px;">[<a href="#pid81">81</a>]</span>We have shown throughout this survey that there is a growing interest in |
| 1072 | and emotion analysis within digital humanities. Given the fact that DH have emerged | 1479 | sentiment and emotion analysis within computational literary studies as one |
| 1073 | into a thriving science within the past decade, it may safely be said that this | 1480 | main field of digital humanities. Given the fact that DH have emerged into a |
| 1074 | direction of research is relatively new. At the same time, the research in sentiment | 1481 | thriving science within the past decade, it may safely be said that this |
| 1075 | analysis started in computational linguistic more than two decades ago and is | 1482 | direction of research is relatively new. It further constitutes an |
| 1076 | nowadays an established field that has dedicated workshops and tracks in the main | 1483 | interesting field that connects literary studies and computational |
| 1077 | computational linguistics conferences. Moreover, a recent meta-study by Mäntylä et | 1484 | linguistics. |
| 1078 | al.<a id="fna123" class="fn" href="#fn123" title="Mäntylä et al. 2018, passim.">[123]</a> shows that the number of | 1485 | </p> |
| 1079 | papers in sentiment analysis is rapidly increasing each year. Indeed, the topic has | 1486 | <p id="pid82"><span style="float: right; padding: .2em; color: #999; border: 1px solid #ccc; background-color: #eee; margin-right: -80px;">[<a href="#pid82">82</a>]</span>In computational linguistics, sentiment analysis started more than two |
| 1080 | not yet outrun itself and we should not expect to see it vanishing within the next | 1487 | decades ago and is nowadays an established field that has dedicated |
| 1081 | decade or two, provided that no significant paradigm shift in the computational | 1488 | workshops and tracks in the main conferences. Moreover, a recent meta-study |
| 1082 | sciences takes place. One may wonder whether the same applies to sentiment analysis | 1489 | by Mäntylä et al.<a id="fna130" class="fn" href="#fn130" title="Mäntylä et al. 2018.">[130]</a> shows |
| 1083 | in digital humanities scholarship. Will the interest in the topic grow continuously | 1490 | that the number of papers in sentiment analysis is rapidly increasing each |
| 1084 | or will it rally to the peak and vanish in a few years? | 1491 | year. Indeed, the topic has not yet outrun itself and we should not expect |
| 1085 | </p> | 1492 | to see it vanishing within the next decade or two. In addition, there are |
| 1086 | <p>There is no decisive answer. The popularity of sentiment analysis may have reached | 1493 | still many open challenges. For each novel representation-learning approach, |
| 1087 | a | 1494 | the question arises how sentiment concepts can be approprietly included. For |
| 1088 | peak but is far from fading. Application-wise, not a lot has changed during the past | 1495 | most languages in the world the number of resources is low and it is not |
| 1089 | years: researchers are still interested in predicting sentiment and emotion from text | 1496 | even known if established approaches could simply be transferred. To |
| 1090 | for different purposes. If anything has changed, it is methodology. Early research | 1497 | leverage these issues, research on multilingual methods that induce models |
| 1091 | in | 1498 | in resource-scarce environments is an interesting modern direction, and a |
| 1092 | sentiment analysis relied on word polarity and specific dictionaries. Modern | 1499 | promising and rewarding field. All these developments on machine learning |
| 1093 | state-of-the-art approaches rely on word embeddings and deep learning architectures. | 1500 | models, domain adaptation, pretraining and fine-tuning will also be |
| 1094 | Having started with simple polarity detection, contemporary sentiment analysis has | 1501 | beneficial for the digital humanities, but we cannot expect that all |
| 1095 | advanced to a more nuanced analysis of sentiments and emotions. | 1502 | particular challenges that arise from research questions in literary studies |
| 1096 | </p> | 1503 | will be solved in this field that focuses on generalizable methods. |
| 1097 | <p>The situation is somewhat different in digital humanities research. Most of the works | 1504 | </p> |
| 1098 | rely on affective lexicons and word counts, a technique for detecting emotions in | 1505 | <p id="pid83"><span style="float: right; padding: .2em; color: #999; border: 1px solid #ccc; background-color: #eee; margin-right: -80px;">[<a href="#pid83">83</a>]</span>Digital humanties has specific needs that cannot be readily addressed by |
| 1099 | literary text first used by Anderson and McMaster in 1982.<a id="fna124" class="fn" href="#fn124" title="Anderson / McMaster 1982, passim.">[124]</a> Even the most recent works base the | 1506 | existing methods or those that are developed in the future, in computational |
| 1100 | interpretation of the results on the use of dictionaries and counts of | 1507 | linguistics, machine learning, and computer science in general. As we have |
| 1101 | emotion-bearing words in a text, passage, or sentence. In fact, around 70% of the | 1508 | seen in this survey, most of the works rely on affective lexicons and word |
| 1102 | papers we discussed in <a title="" href="#hd8">Section 4</a> substantially rely on the use of various lexical | 1509 | counts, a technique for detecting emotions in literary text first used by |
| 1103 | resources for detecting emotions (see <a title="" href="#emotion_analysis_2019_003"><span class="medium">Table 1</span></a> for a summary of methods used in the | 1510 | Anderson and McMaster in 1982.<a id="fna131" class="fn" href="#fn131" title="Anderson / McMaster 1982.">[131]</a> Even the most recent works base the interpretation of the |
| 1104 | reviewed papers). We have discussed some limitations of this approach in <a title="" href="#hd12">Section 4.2</a>. | 1511 | results on the use of dictionaries and counts of emotion-bearing words in a |
| 1105 | Let us reiterate its weakness with the following small example. Consider the sentence | 1512 | text, passage, or sentence. In fact, around 70 % of the papers we discussed |
| 1106 | ›Jack was afraid of John because John held a knife in his hand‹. Assuming a | 1513 | in <a title="" href="#hd10">section 4</a> substantially rely on the |
| 1107 | dictionary of emotion-bearing words is used, the sentence can be categorized as | 1514 | use of various lexical resources for detecting emotions. We identify a set |
| 1108 | expressing <i>fear</i>, because of the two strong fear markers, <i>afraid</i> and <i>knife</i>. Indeed, the sentence | 1515 | of particular challenges that hold for digital humanities and computational |
| 1109 | does express <i>fear</i>. But does it do it equally for Jack and | 1516 | literary studies and that are presumable reasons for that choice. |
| 1110 | John? The answer is no: Jack is the one who is afraid and John holding a knife is | 1517 | </p> |
| 1111 | the | 1518 | <p id="pid84"><span style="float: right; padding: .2em; color: #999; border: 1px solid #ccc; background-color: #eee; margin-right: -80px;">[<a href="#pid84">84</a>]</span><i>The object of research is the central element. </i>In |
| 1112 | reason for Jack being afraid. Let us assume that a researcher is interested in the | 1519 | contrast to computational linguistics, the goal of digital humanities is not |
| 1113 | emotion analysis of a book that contains thousands of sentences expressing emotions | 1520 | to develop generalizable methods. The goal is, instead, to develop those |
| 1114 | in different ways: some sentences describe characters who feel emotions just as in | 1521 | methods that are helpful for a particular research question; and in contrast |
| 1115 | the sentence above, some are narrator’s digressions filled with emotions, some | 1522 | to computational linguistics, this includes tasks that only very few people |
| 1116 | contain emotion-bearing words (<i>knife</i>, <i>baby</i>) but do not in fact express the same emotion in any given context. No | 1523 | work on. It would be a huge advantage if those methods could be generalized |
| 1117 | doubt, a dictionary and count-based approach will be helpful in understanding the | 1524 | and reused, however, it is not a primary goal. Instead, an emotion analysis |
| 1118 | distribution of the emotion lexicon throughout the story. But is it enough for the | 1525 | method for a particular scholar who analyzes texts from a particular subset, |
| 1119 | interpretation? Can hermeneutics, in its traditional form, make use of such | 1526 | for instance genre, period, or author needs to work well for this subset. It |
| 1120 | knowledge? Barely. In fact, some of the works that we reviewed pinpoint that the | 1527 | might not be feasable to develop sophisticated deep learning methods for |
| 1121 | surface affective value of the words does not always align with their more nuanced | 1528 | each of these approaches, but just to be used once. |
| 1122 | affective meaning and that sentiment analysis tools make mistakes when classifying | 1529 | </p> |
| 1123 | a | 1530 | <p id="pid85"><span style="float: right; padding: .2em; color: #999; border: 1px solid #ccc; background-color: #eee; margin-right: -80px;">[<a href="#pid85">85</a>]</span><i>Transparency of the computational method is not a bonus; |
| 1124 | text as emotional or not.<a id="fna125" class="fn" href="#fn125" title="Reed 2018, passim.">[125]</a> If so, how reliable | 1531 | it is a crucial property.</i> In digital humanities, research is often |
| 1125 | is the interpretation? In other words, what kind of interpretation should we expect | 1532 | exploratory. The application of an existing method on a corpus can lead to |
| 1126 | from the sentiment and emotion analysis research in the DH community? | 1533 | new findings, but it is common that an interactive application of a method |
| 1127 | </p> | 1534 | to explore a phenomenon is even more promising. Such interactive application |
| 1128 | <p>We do not have a ready answer to that question. At the one extreme, there is | 1535 | requires full control by the user in real time – and that is something that |
| 1129 | traditional hermeneutics, the examples of which are presented in a <a title="" href="#hd7">Section 3</a>. At the | 1536 | pretrained deep neural methods cannot (yet) provide. However, emotion |
| 1130 | other extreme, there is interpretation in the form of ›Author A writes with more | 1537 | lexicons that point to particular aspects in the text in a transparent |
| 1131 | emotion than author B because the numbers say so‹. We do, however, suggest that a | 1538 | manner do, despite of their disadvantages. |
| 1132 | balance should be made somewhere between these two extremes. Even as simple as it | 1539 | </p> |
| 1133 | is, | 1540 | <p id="pid86"><span style="float: right; padding: .2em; color: #999; border: 1px solid #ccc; background-color: #eee; margin-right: -80px;">[<a href="#pid86">86</a>]</span><i>Computational expertise is not sufficient in an |
| 1134 | the approach of detecting sentiment and emotion-related words can be used to deliver | 1541 | interdisciplinary research field. </i>In computational research |
| 1135 | a high-quality interpretation such as in Heuser et al.<a id="fna126" class="fn" href="#fn126" title="Heuser et al. 2016, passim.">[126]</a> or Morin and Acerbi.<a id="fna127" class="fn" href="#fn127" title="Morin and Acerbi 2017, passim.">[127]</a> However, we note again that there are still limits posed by the | 1542 | disciplines, a minimum amount of understanding of the respective domain is |
| 1136 | simplicity of this approach. | 1543 | helpful but not necessarily (always) required. Particularly in recent years, |
| 1137 | </p> | 1544 | with the development of end-to-end learning methods that hardly explain |
| 1138 | <p>This leads us to an outline of the reality of sentiment analysis research in digital | 1545 | decisions, it became common to purely rely on performance measures (though |
| 1139 | humanities: the methods of sentiment analysis used by some of the DH scholars | 1546 | this changes with recent research on explainable artificial intelligence). |
| 1140 | nowadays have gone or are almost extinct among computational linguists. This in turn | 1547 | In contrast, in computational literary studies, knowledge of the domain is |
| 1141 | affects the quality of the interpretation. | 1548 | required. Without it, research questions cannot be answered. This is not a |
| 1142 | </p> | 1549 | unique property of digital humanities as an interdisciplnary field. However, |
| 1143 | <p>However, we admit that this criticism may be unfair. In fact, there is a possible | 1550 | it is particularly challenging here, given its recent growth, fast |
| 1144 | reason why DH researchers have taken this approach to sentiment analysis. Digital | 1551 | development, and also the differences in the research culture between |
| 1145 | humanities are still being formed as an independent discipline and it is easier to | 1552 | humanities and computer science (which are arguably smaller between, for |
| 1146 | form something new in a step-by-step fashion. Resorting to a metaphor from the | 1553 | instance, natural sciences and computer science, to which fields like |
| 1147 | construction world, one should first learn how to stack single bricks to build a wall | 1554 | computational chemistry or bioinformatics belong). |
| 1148 | rather than starting from the design of a communications system. It is necessary to | 1555 | </p> |
| 1149 | make sure that appropriate tools and methods are chosen instead of using what proved | 1556 | <p id="pid87"><span style="float: right; padding: .2em; color: #999; border: 1px solid #ccc; background-color: #eee; margin-right: -80px;">[<a href="#pid87">87</a>]</span>This leads to a set of challenges that need to be addressed, while developing |
| 1150 | to be successful in other domains without reflection. It is true that much digital | 1557 | methods further. In contrast to most emotion analysis work in other domains |
| 1151 | humanities research (especially dealing with text) uses the methods of text analysis | 1558 | (like social media or news), the unit of analysis should be larger. It is |
| 1152 | that were in fashion in computational linguistic twenty years ago. One may argue that | 1559 | not sufficient to only analyze sentences in isolation (or even just words). |
| 1153 | new research in digital humanities should start with the <span style="color:#035151"><i>state-of-the-art methods</i></span>. Indeed, some arguments that methodology is at | 1560 | Instead, the overall development of characters, the story line as a whole |
| 1154 | the root of the interpretation have already been made.<a id="fna128" class="fn" href="#fn128" title="Da 2019, passim.">[128]</a> So, if there is anything that digital humanities can learn from | 1561 | need to be considered. This is a research direction that hardly received any |
| 1155 | computational linguistics, it is that methodology cannot stall. What really matters | 1562 | attention yet; presumably because of technical challenges, but likely also |
| 1156 | for digital humanities is interpretation, and if methodology is not going forward, | 1563 | due to the lack of annotated corpora that would be required to contain |
| 1157 | the interpretation is not either. | 1564 | annotations on different levels. Further, these annotations need particular |
| 1158 | </p> | 1565 | expertise from the annotators. It is not feasible to show an entire book to |
| 1159 | </div> | 1566 | workers on a crowdsourcing platform to receive annotations on fine-grained |
| 1160 | <div id="chapter"><a name="hd26"> </a><h2> | 1567 | levels (for characters and their developments). Therefore, for domains of |
| 1568 | interest, we point out that the development of corpora in computational | ||
| 1569 | literary studies are expected to be more expensive and will take longer than | ||
| 1570 | in other fields in which emotion analysis is applied. | ||
| 1571 | </p> | ||
| 1572 | <p id="pid88"><span style="float: right; padding: .2em; color: #999; border: 1px solid #ccc; background-color: #eee; margin-right: -80px;">[<a href="#pid88">88</a>]</span>Finally, we believe that the integration of psychological models into | ||
| 1573 | computational approaches in literature studies is important. Literature | ||
| 1574 | contains representations of whole worlds, the depictions are more | ||
| 1575 | comprehensive than in news articles or social media. This also requires a | ||
| 1576 | deeper understanding of described social processes and (imagined) mental | ||
| 1577 | states. | ||
| 1578 | </p> | ||
| 1579 | <p id="pid89"><span style="float: right; padding: .2em; color: #999; border: 1px solid #ccc; background-color: #eee; margin-right: -80px;">[<a href="#pid89">89</a>]</span>And finally, the role of the experiencer of an emotion needs to be considered | ||
| 1580 | more than in other fields. While on Twitter analysis, we typically care | ||
| 1581 | about the emotion that the author of a message felt while writing it, we | ||
| 1582 | typically do not care about the emotion of the author of a novel, while | ||
| 1583 | writing it.<a id="fna132" class="fn" href="#fn132" title="Oberländer et al. 2020.">[132]</a> Instead, we | ||
| 1584 | are faced with the more challenging task to attribute emotions to characters | ||
| 1585 | or even infer the emotions that might be developed by readers of a text. | ||
| 1586 | </p> | ||
| 1587 | <p id="pid90"><span style="float: right; padding: .2em; color: #999; border: 1px solid #ccc; background-color: #eee; margin-right: -80px;">[<a href="#pid90">90</a>]</span>In summary, we believe that the field of emotion analysis for literary | ||
| 1588 | studies has still space for research in multiple directions. The main | ||
| 1589 | challenge will be to identify the particular challenges of literare and | ||
| 1590 | develop methods for these text genres, instead of using existing methods | ||
| 1591 | that have developed with the purpose in mind of being generalizing across | ||
| 1592 | application areas. | ||
| 1593 | </p> | ||
| 1594 | </div><a name="div30"> </a><div id="chapter"><a name="hd28"> </a><h2> | ||
| 1161 | <div style="position:relative;width:90%;">Acknowledgements</div> | 1595 | <div style="position:relative;width:90%;">Acknowledgements</div> |
| 1162 | </h2> | 1596 | </h2> |
| 1163 | <p> | 1597 | <p id="pid91"><span style="float: right; padding: .2em; color: #999; border: 1px solid #ccc; background-color: #eee; margin-right: -80px;">[<a href="#pid91">91</a>]</span>We thank Laura Ana Maria Oberländer, Sebastian Padó, and Enrica Troiano for |
| 1164 | We thank Laura Ana Maria Bostan, Sebastian Padó, and Enrica Troiano | 1598 | fruitful discussions and the ZfDG team for their help in preparation of this |
| 1165 | for fruitful discussions and the ZfDG team for their help in preparation of this | 1599 | article. This research has been conducted within the <a href="www.creta.uni-stuttgart.de\" target="_blank">CRETA</a> project which is funded by the German Ministry for |
| 1166 | article. This research has been conducted within the <a href="http:\www.creta. uni-stuttgart.de" target="_blank">CRETA</a> project which is funded by the German Ministry for Education and | 1600 | Education and Research (BMBF) and partially funded by the German Research |
| 1167 | Research (BMBF) and partially funded by the German Research Council (DFG), projects | 1601 | Council (DFG), projects SEAT (Structured Multi-Domain Emotion Analysis from |
| 1168 | SEAT (Structured Multi-Domain Emotion Analysis from Text, KL 2869/1-1). | 1602 | Text, KL 2869/1-1). We further thank the anonymous reviewers for their |
| 1603 | helpful comments on an earlier version of this article. | ||
| 1169 | </p> | 1604 | </p> |
| 1173 | <hr><a name="footnotes"></a><div class="footnote"> | 1608 | <hr><a name="footnotes"></a><div class="footnote"> |
| 1174 | <h2> | 1609 | <h2>Fußnoten</h2> |
| 1175 | <ul class="footnote"> | 1610 | <ul class="footnote"> |
| 1178 | </div> | 1613 | </div> |
| 1179 | <div class="footnote3"><a title="Bing Liu: Sentiment Analysis: mining opinions, sentiments, and emotions. New York, NY 2015." href="#liu_opinions_2015">Liu 2015</a>, p.2. | 1614 | <div class="footnote3"><a title="Bing Liu: Sentiment Analysis: mining opinions, sentiments, and emotions. New York, NY 2015." href="#liu_opinions_2015">Liu 2015</a>, |
| 1615 | p. 2. | ||
| 1180 | </div> | 1616 | </div> |
| 1183 | </div> | 1619 | </div> |
| 1184 | <div class="footnote3"><a title="Mohammad Soleymani / David Garcia / Brendan Jou / Björn Schuller / Shih-Fu Chang / Maja Pantic: A survey of multimodal sentiment analysis. In: Image and Vision Computing 65 (2017), pp. 3–14." href="#soleymani_survey_2017">Soleymani et al. 2017</a>. | 1620 | <div class="footnote3"><a title="Mohammad Soleymani / David Garcia / Brendan Jou / Björn Schuller / Shih-Fu Chang / Maja Pantic: A survey of multimodal sentiment analysis. In: Image and Vision Computing 65 (2017), pp. 3–14." href="#soleymani_survey_2017">Soleymani et al. |
| 1621 | 2017</a>. | ||
| 1185 | </div> | 1622 | </div> |
| 1188 | </div> | 1625 | </div> |
| 1189 | <div class="footnote3"><a title=" | 1626 | <div class="footnote3"><a title="Janyce Wiebe / Theresa Wilson / Rebecca Bruce / Matthew Bell / Melanie Martin: Learning Subjective Language. In: Computational Linguistics 30 (2004), pp. 277–308." href="#wiebe_language_2004">Wiebe et al. 2004</a>. |
| 1190 | </div> | 1627 | </div> |
| 1193 | </div> | 1630 | </div> |
| 1194 | <div class="footnote3"><a title=" | 1631 | <div class="footnote3"><a title="Klaus R. Scherer: What are emotions? And how can they be measured? In: Social Science Information 44 (2005), i. 4, pp. 695–729." href="#scherer_emotions_2005">Scherer 2005</a>, p. 1. |
| 1195 | </div> | 1632 | </div> |
| 1198 | </div> | 1635 | </div> |
| 1199 | <div class="footnote3"><a title=" | 1636 | <div class="footnote3"><a title="Andrea Scarantino: The Philosophy of Emotions and Its Impact on Affective Sciences. In: Handbook of emotions. Ed. by Lisa Feldman Barret / Michael Lewis / Jeannette M. Haviland-Jones. 4. edition. New York, NY et al. 2016. pp. 3–49." href="#scarantino_philosophy_2016">Scarantino 2016</a>, p. 36. |
| 1200 | </div> | 1637 | </div> |
| 1203 | </div> | 1640 | </div> |
| 1641 | <div class="footnote3"><a title="John D. Mayer / Richard D. Roberts / Sigal G. Barsade: Human abilities: Emotional intelligence. In: Annual Review of Psychology 59 (2008), i. 1, pp. 507–536." href="#mayer_abilities_2008">Mayer et al. 2008</a>, p. 2. | ||
| 1642 | </div> | ||
| 1643 | </li><br><li class="footnote"> | ||
| 1644 | <div class="footnote2" id="fn7" href="#fna7">[<a href="#fna7">7</a>] | ||
| 1645 | </div> | ||
| 1204 | <div class="footnote3"><a title="Nan Z. Da: The computational case against computational literary studies. In: Critical Inquiry 45 (2019), i. 3, pp. 601–639." href="#da_case_2019">Da 2019</a>, p. 602. | 1646 | <div class="footnote3"><a title="Nan Z. Da: The computational case against computational literary studies. In: Critical Inquiry 45 (2019), i. 3, pp. 601–639." href="#da_case_2019">Da 2019</a>, p. 602. |
| 1206 | </li><br><li class="footnote"> | 1648 | </li><br><li class="footnote"> |
| 1207 | <div class="footnote2" id=" | 1649 | <div class="footnote2" id="fn8" href="#fna8">[<a href="#fna8">8</a>] |
| 1208 | </div> | 1650 | </div> |
| 1211 | </li><br><li class="footnote"> | 1653 | </li><br><li class="footnote"> |
| 1212 | <div class="footnote2" id="fn8" href="#fna8">[<a href="#fna8">8</a>] | ||
| 1213 | </div> | ||
| 1214 | <div class="footnote3"><a title="David Lowell Hoover / Jonathan Culpeper / Kieran O’Halloran: Digital literary studies: Corpus Approaches to Poetry, Prose, and Drama. New York, NY 2014." href="#hoover_studies_2014">Hoover et al. 2014</a>. | ||
| 1215 | </div> | ||
| 1216 | </li><br><li class="footnote"> | ||
| 1217 | <div class="footnote2" id="fn9" href="#fna9">[<a href="#fna9">9</a>] | 1654 | <div class="footnote2" id="fn9" href="#fna9">[<a href="#fna9">9</a>] |
| 1218 | </div> | 1655 | </div> |
| 1219 | <div class="footnote3"><a title="Norbert Schwarz: Emotion, cognition, and decision making. In: Cognition & Emotion 14 (2000), i. 4, pp. 433–440." href="#schwarz_emotion_2000">Schwarz 2000</a>, p. 433. | 1656 | <div class="footnote3"><a title="David Lowell Hoover / Jonathan Culpeper / Kieran O’Halloran: Digital literary studies: Corpus Approaches to Poetry, Prose, and Drama. New York, NY 2014." href="#hoover_studies_2014">Hoover et al. |
| 1657 | 2014</a>. | ||
| 1220 | </div> | 1658 | </div> |
| 1223 | </div> | 1661 | </div> |
| 1224 | <div class="footnote3"><a title=" | 1662 | <div class="footnote3"> E.g. <a title="Mostafa Al Masum Shaikh / Helmut Prendinger / Mitsuru Ishizuka: A Linguistic Interpretation of the OCC Emotion Model for Affect Sensing from Text. In: Affective Information Processing. Ed. by Jianhua Tao / Tieniu Tan. London 2009." href="#shaikh_interpretation_2009">Shaikh 2009</a>. |
| 1225 | | 1663 | |
| 1226 | </div> | 1664 | </div> |
| 1228 | <div class="footnote2" id="fn11" href="#fna11">[<a href="#fna11">11</a>] | 1666 | <div class="footnote2" id="fn11" href="#fna11">[<a href="#fna11">11</a>] |
| 1667 | </div> | ||
| 1668 | <div class="footnote3"><a title="Norbert Schwarz: Emotion, cognition, and decision making. In: Cognition & Emotion 14 (2000), i. 4, pp. 433–440." href="#schwarz_emotion_2000">Schwarz 2000</a>, | ||
| 1669 | p. 433. | ||
| 1670 | </div> | ||
| 1671 | </li><br><li class="footnote"> | ||
| 1672 | <div class="footnote2" id="fn12" href="#fna12">[<a href="#fna12">12</a>] | ||
| 1673 | </div> | ||
| 1674 | <div class="footnote3"><a title="Philip Nicholas Johnson-Laird / Keith Oatley: Emotions in Music, Literature, and Film. In: Handbook of emotions. Ed. by Lisa Feldman Barret / Michael Lewis / Jeannette M. Haviland-Jones. 4. edition. New York, NY et al. 2016. pp. 82–97." href="#johnson_emotions_2016">Johnson-Laird / | ||
| 1675 | Oatley 2016</a>; <a title="Maja Djikic / Keith Oatley / Sara Zoeterman / Jordan B. Peterson: On being moved by art: How reading fiction transforms the self. In: Creativity Research Journal 21 (2009), i. 1, pp. 24–29." href="#djikic_art_2009">Djikic et al. 2009</a>. | ||
| 1676 | </div> | ||
| 1677 | </li><br><li class="footnote"> | ||
| 1678 | <div class="footnote2" id="fn13" href="#fna13">[<a href="#fna13">13</a>] | ||
| 1229 | </div> | 1679 | </div> |
| 1231 | <a title="Patrick Colm Hogan: Fictions and feelings: On the place of literature in the study of emotion. In: Emotion Review 2 (2010), i. 2, pp. 184–195." href="#hogan_fictions_2010">Hogan 2010</a>; | 1681 | <a title="Patrick Colm Hogan: Fictions and feelings: On the place of literature in the study of emotion. In: Emotion Review 2 (2010), i. 2, pp. 184–195." href="#hogan_fictions_2010">Hogan 2010</a>; |
| 1232 | <a title="Patrick Colm Hogan: What Literature Teaches Us about Emotion. New York, NY 2011." href="#hogan_literature_2011">Hogan 2011</a>; | 1682 | <a title="Patrick Colm Hogan: What Literature Teaches Us about Emotion. New York, NY 2011." href="#hogan_literature_2011">Hogan |
| 1233 | <a title="P. Matthijs Bal / Martijn Veltkamp: How does fiction reading influence empathy? An experimental investigation on the role of emotional transportation. In: PLOS ONE 8 (2013), i. 1, p. e55341. Article from 30.01.2013. DOI: 10.1371/journal.pone.0055341" href="#bal_fiction_2013">Bal / Veltkamp 2013</a>; | 1683 | 2011</a>; <a title="P. Matthijs Bal / Martijn Veltkamp: How does fiction reading influence empathy? An experimental investigation on the role of emotional transportation. In: PLOS ONE 8 (2013), i. 1, p. e55341. Article from 30.01.2013. DOI: 10.1371/journal.pone.0055341" href="#bal_fiction_2013">Bal / |
| 1234 | <a title="Maja Djikic / Keith Oatley / Mihnea C. Moldoveanu: Reading other minds: Effects of literature on empathy. In: Scientific Study of Literature 3 (2013), i. 1, pp. 28–47." href="#djikic_minds_2013">Djikic et al. 2013</a>; | 1684 | Veltkamp 2013</a>; <a title="Maja Djikic / Keith Oatley / Mihnea C. Moldoveanu: Reading other minds: Effects of literature on empathy. In: Scientific Study of Literature 3 (2013), i. 1, pp. 28–47." href="#djikic_minds_2013">Djikic et al. 2013</a>; <a title="Dan R. Johnson: Transportation into a story increases empathy, prosocial behavior, and perceptual bias toward fearful expressions. In: Personality and Individual Differences 52 (2012), i. 2, pp. 150–155." href="#johnson_transportation_2012">Johnson 2012</a>; <a title="Dalya Samur / Mattie Tops / Sander L. Koole: Does a single session of reading literary fiction prime enhanced mentalising performance? Four replication experiments of Kidd and Castano (2013). In: Cognition & Emotion 32 (2018), pp. 130–144." href="#samur_session_2018">Samur et al. |
| 1235 | <a title="Dan R. Johnson: Transportation into a story increases empathy, prosocial behavior, and perceptual bias toward fearful expressions. In: Personality and Individual Differences 52 (2012), i. 2, pp. 150–155." href="#johnson_transportation_2012">Johnson 2012</a>; | 1685 | 2018.</a></div> |
| 1236 | <a title="Dalya Samur / Mattie Tops / Sander L. Koole: Does a single session of reading literary fiction prime enhanced mentalising performance? Four replication experiments of Kidd and Castano (2013). In: Cognition & Emotion 32 (2018), pp. 130–144." href="#samur_session_2018">Samur et al. 2018.</a></div> | ||
| 1237 | </li><br><li class="footnote"> | ||
| 1238 | <div class="footnote2" id="fn12" href="#fna12">[<a href="#fna12">12</a>] | ||
| 1239 | </div> | ||
| 1240 | <div class="footnote3"><a title="Dolf Zillmann / Richard T. Hezel / Norman J. Medoff: The effect of affective states on selective exposure to televised entertainment fare. In: Journal of Applied Social Psychology 10 (1980), i. 4, pp. 323–339." href="#zillmann_effect_1980">Zillmann et al. 1980</a>; | ||
| 1241 | <a title="Catherine Sheldrick Ross: Finding without seeking: the information encounter in the context of reading for pleasure. In: Information Processing & Management 35 (1999), i. 6., pp. 783–799." href="#ross_encounter_1999">Ross 1999</a>; | ||
| 1242 | <a title="Jennings Bryant / Dolf Zillmann: Using television to alleviate boredom and stress: Selective exposure as a function of induced excitational states. In: Journal of Broadcasting & Electronic Media 28 (1984), i. 1, pp. 1–20." href="#bryant_television_1984">Bryant / Zillmann 1984</a>; | ||
| 1243 | <a title="Mary Beth Oliver: Tender affective states as predictors of entertainment preference. In: Journal of Communication 58 (2008), i. 1, pp. 40–61." href="#oliver_states_2008">Oliver 2008</a>; | ||
| 1244 | <a title="Raymond A. Mar / Keith Oatley / Maja Djikic / Justin Mullin: Emotion and narrative fiction: Interactive influences before, during, and after reading. In: Cognition & Emotion 25 (2011), i. 5, pp. 818–833." href="#mar_emotion_2011">Mar et al. | ||
| 1245 | 2011.</a></div> | ||
| 1246 | </li><br><li class="footnote"> | ||
| 1247 | <div class="footnote2" id="fn13" href="#fna13">[<a href="#fna13">13</a>] | ||
| 1248 | </div> | ||
| 1249 | <div class="footnote3"><a title="Plato: Plato in Twelve Volumes. Cambridge, MA 1969. Siehe auch" href="#plato_volumes_1969">Plato 1969</a> | ||
| 1250 | , passim. | ||
| 1251 | </div> | ||
| 1252 | </li><br><li class="footnote"> | 1686 | </li><br><li class="footnote"> |
| 1254 | </div> | 1688 | </div> |
| 1255 | <div class="footnote3"><a title="Aristotle: Poetics. Penguin 1996. (= Penguin Classics)" href="#aristotele_poetics_1996">Aristotle 1996</a>, passim. | 1689 | <div class="footnote3"><a title="Dolf Zillmann / Richard T. Hezel / Norman J. Medoff: The effect of affective states on selective exposure to televised entertainment fare. In: Journal of Applied Social Psychology 10 (1980), i. 4, pp. 323–339." href="#zillmann_effect_1980">Zillmann et al. |
| 1256 | </div> | 1690 | 1980</a>; <a title="Catherine Sheldrick Ross: Finding without seeking: the information encounter in the context of reading for pleasure. In: Information Processing & Management 35 (1999), i. 6., pp. 783–799." href="#ross_encounter_1999">Ross |
| 1691 | 1999</a>; <a title="Jennings Bryant / Dolf Zillmann: Using television to alleviate boredom and stress: Selective exposure as a function of induced excitational states. In: Journal of Broadcasting & Electronic Media 28 (1984), i. 1, pp. 1–20." href="#bryant_television_1984">Bryant / Zillmann 1984</a>; <a title="Mary Beth Oliver: Tender affective states as predictors of entertainment preference. In: Journal of Communication 58 (2008), i. 1, pp. 40–61." href="#oliver_states_2008">Oliver 2008</a>; <a title="Raymond A. Mar / Keith Oatley / Maja Djikic / Justin Mullin: Emotion and narrative fiction: Interactive influences before, during, and after reading. In: Cognition & Emotion 25 (2011), i. 5, pp. 818–833." href="#mar_emotion_2011">Mar et al. 2011.</a></div> | ||
| 1257 | </li><br><li class="footnote"> | 1692 | </li><br><li class="footnote"> |
| 1259 | </div> | 1694 | </div> |
| 1260 | <div class="footnote3"><a title=" | 1695 | <div class="footnote3"><a title="Plato: Plato in Twelve Volumes. Cambridge, MA 1969. Siehe auch" href="#plato_volumes_1969">Plato 1969</a>. |
| 1261 | </div> | 1696 | </div> |
| 1264 | </div> | 1699 | </div> |
| 1265 | <div class="footnote3"><a title=" | 1700 | <div class="footnote3"><a title="Aristotle: Poetics. Penguin 1996. (= Penguin Classics)" href="#aristotle_poetics_1996">Aristotle 1996</a>. |
| 1266 | </div> | 1701 | </div> |
| 1269 | </div> | 1704 | </div> |
| 1270 | <div class="footnote3"><a title="Clifford W. Anderson / George E. McMaster: Modeling emotional tone in stories using tension levels and categorical states. In: Computers and the Humanities 20 (1986), i. 1, pp. 3–9." href="#anderson_tone_1986">Anderson / McMaster 1986</a>, p. 3; | 1705 | <div class="footnote3"><a title="Ronald de Sousa / Andrea Scarantino: Emotion. In: The Stanford Encyclopedia of Philosophy. Ed. by Edward N. Zalta. Stanford, CA 2018. Article from 25.09.2018. [online]" href="#sousa_emotion_2018">de Sousa / |
| 1271 | <a title="Patrick Colm Hogan: Fictions and feelings: On the place of literature in the study of emotion. In: Emotion Review 2 (2010), i. 2, pp. 184–195." href="#hogan_fictions_2010">Hogan 2010</a>, p. 187; <a title="Andrew Piper / Richard Jean So: Quantifying the weepy bestseller. In: The New Rebublic. Article from 18.12.2015. [online]" href="#piper_bestseller_2015">Piper / | 1706 | Scarantino 2018</a>. |
| 1272 | Jean So 2015</a>. | ||
| 1273 | </div> | 1707 | </div> |
| 1276 | </div> | 1710 | </div> |
| 1711 | <div class="footnote3"><a title="Leo Tolstoy: What is art? And essays on art. Harmondsworth 1962. (= Penguin classics) Siehe auch" href="#tolstoy_art_1962">Tolstoy 1962</a>. | ||
| 1712 | </div> | ||
| 1713 | </li><br><li class="footnote"> | ||
| 1714 | <div class="footnote2" id="fn19" href="#fna19">[<a href="#fna19">19</a>] | ||
| 1715 | </div> | ||
| 1716 | <div class="footnote3"><a title="Clifford W. Anderson / George E. McMaster: Modeling emotional tone in stories using tension levels and categorical states. In: Computers and the Humanities 20 (1986), i. 1, pp. 3–9." href="#anderson_tone_1986">Anderson / McMaster | ||
| 1717 | 1986</a>, p. 3; <a title="Patrick Colm Hogan: Fictions and feelings: On the place of literature in the study of emotion. In: Emotion Review 2 (2010), i. 2, pp. 184–195." href="#hogan_fictions_2010">Hogan 2010</a>, p. 187; <a title="Andrew Piper / Richard Jean So: Quantifying the weepy bestseller. In: The New Rebublic. Article from 18.12.2015. [online]" href="#piper_bestseller_2015">Piper / Jean So 2015</a>. | ||
| 1718 | </div> | ||
| 1719 | </li><br><li class="footnote"> | ||
| 1720 | <div class="footnote2" id="fn20" href="#fna20">[<a href="#fna20">20</a>] | ||
| 1721 | </div> | ||
| 1277 | <div class="footnote3"><a title="Richard A. Lanham: The electronic word: Literary study and the digital revolution. In: New Literary History 20 (1989), i. 2, pp. 265–290." href="#lanham_word_1989">Lanham 1989</a>. | 1722 | <div class="footnote3"><a title="Richard A. Lanham: The electronic word: Literary study and the digital revolution. In: New Literary History 20 (1989), i. 2, pp. 265–290." href="#lanham_word_1989">Lanham 1989</a>. |
| 1279 | </li><br><li class="footnote"> | 1724 | </li><br><li class="footnote"> |
| 1280 | <div class="footnote2" id="fn19" href="#fna19">[<a href="#fna19">19</a>] | ||
| 1281 | </div> | ||
| 1282 | <div class="footnote3"><a title="David M. Berry: Introduction: Understanding the digital humanities. In: Understanding digital humanities. Ed. by David M. Berry. Houndmills et al. 2012, pp. 1–20." href="#berry_introduction_2012">Berry 2012</a>; <a title="Susan Schreibman / Ray Siemens / John Unsworth: A New Companion to Digital Humanities. Chichester et al. 2015/2016." href="#schreibman_compainion_2016">Schreibman et al. 2015</a>. | ||
| 1283 | </div> | ||
| 1284 | </li><br><li class="footnote"> | ||
| 1285 | <div class="footnote2" id="fn20" href="#fna20">[<a href="#fna20">20</a>] | ||
| 1286 | </div> | ||
| 1287 | <div class="footnote3"><a title="Edward Vanhoutte: The gates of hell: History and definition of digital|humanities|computing. In: Defining Digital Humanities. A Reader. Ed. by Meliss Terras / Julianne Hyhan / Edward Vanhoutte. Farnham 2013, pp. 119–156." href="#vanhoutte_gates_2013">Vanhoutte 2013</a>, p. 142; | ||
| 1288 | <a title="Matthew Lee Jockers / Ted Underwood: Text-mining the humanities. In: A New Companion to Digital Humanities. Ed. by Susan Schreibman / Ray Siemens / John Unsworth. Pondicherry 2016, pp. 291–306." href="#jockers_humanities_2016">Jockers / Underwood | ||
| 1289 | 2016</a>, pp. 292f. | ||
| 1290 | </div> | ||
| 1291 | </li><br><li class="footnote"> | ||
| 1292 | <div class="footnote2" id="fn21" href="#fna21">[<a href="#fna21">21</a>] | 1725 | <div class="footnote2" id="fn21" href="#fna21">[<a href="#fna21">21</a>] |
| 1726 | </div> | ||
| 1727 | <div class="footnote3"><a title="David M. Berry: Introduction: Understanding the digital humanities. In: Understanding digital humanities. Ed. by David M. Berry. Houndmills et al. 2012, pp. 1–20." href="#berry_introduction_2012">Berry 2012</a>; | ||
| 1728 | <a title="Susan Schreibman / Ray Siemens / John Unsworth: A New Companion to Digital Humanities. Chichester et al. 2015/2016." href="#schreibman_compainion_2016">Schreibman | ||
| 1729 | et al. 2015</a>. | ||
| 1730 | </div> | ||
| 1731 | </li><br><li class="footnote"> | ||
| 1732 | <div class="footnote2" id="fn22" href="#fna22">[<a href="#fna22">22</a>] | ||
| 1733 | </div> | ||
| 1734 | <div class="footnote3"><a title="Edward Vanhoutte: The gates of hell: History and definition of digital|humanities|computing. In: Defining Digital Humanities. A Reader. Ed. by Meliss Terras / Julianne Hyhan / Edward Vanhoutte. Farnham 2013, pp. 119–156." href="#vanhoutte_gates_2013">Vanhoutte | ||
| 1735 | 2013</a>, p. 142; <a title="Matthew Lee Jockers / Ted Underwood: Text-mining the humanities. In: A New Companion to Digital Humanities. Ed. by Susan Schreibman / Ray Siemens / John Unsworth. Pondicherry 2016, pp. 291–306." href="#jockers_humanities_2016">Jockers / Underwood 2016</a>, pp. | ||
| 1736 | 292f. | ||
| 1737 | </div> | ||
| 1738 | </li><br><li class="footnote"> | ||
| 1739 | <div class="footnote2" id="fn23" href="#fna23">[<a href="#fna23">23</a>] | ||
| 1293 | </div> | 1740 | </div> |
| 1297 | </li><br><li class="footnote"> | 1744 | </li><br><li class="footnote"> |
| 1298 | <div class="footnote2" id="fn22" href="#fna22">[<a href="#fna22">22</a>] | ||
| 1299 | </div> | ||
| 1300 | <div class="footnote3"><a title="Charles Darwin: The expression of emotion in animals and man. London 1872." href="#darwin_expression_1872">Darwin 1872</a>, passim. | ||
| 1301 | </div> | ||
| 1302 | </li><br><li class="footnote"> | ||
| 1303 | <div class="footnote2" id="fn23" href="#fna23">[<a href="#fna23">23</a>] | ||
| 1304 | </div> | ||
| 1305 | <div class="footnote3"><a title="Maria Gendron / Lisa Feldman Barrett: Reconstructing the past: A century of ideas about emotion in psychology. In: Emotion review 1 (2009), i. 4, pp. 316–339." href="#gendrin_past_2009">Gendron / Feldman Barrett 2009</a>. | ||
| 1306 | </div> | ||
| 1307 | </li><br><li class="footnote"> | ||
| 1308 | <div class="footnote2" id="fn24" href="#fna24">[<a href="#fna24">24</a>] | 1745 | <div class="footnote2" id="fn24" href="#fna24">[<a href="#fna24">24</a>] |
| 1309 | </div> | 1746 | </div> |
| 1310 | <div class="footnote3"><a title=" | 1747 | <div class="footnote3"><a title="Charles Darwin: The expression of emotion in animals and man. London 1872." href="#darwin_expression_1872">Darwin 1872</a>. |
| 1311 | </div> | 1748 | </div> |
| 1314 | </div> | 1751 | </div> |
| 1315 | <div class="footnote3"><a title=" | 1752 | <div class="footnote3"><a title="Maria Gendron / Lisa Feldman Barrett: Reconstructing the past: A century of ideas about emotion in psychology. In: Emotion review 1 (2009), i. 4, pp. 316–339." href="#gendrin_past_2009">Gendron / Feldman Barrett 2009</a>. |
| 1316 | </div> | 1753 | </div> |
| 1319 | </div> | 1756 | </div> |
| 1757 | <div class="footnote3"><a title="Silvan Tomkins: Affect imagery consciousness. 4 vol. New York, NY et al. 1962. Vol. I: The positive affects." href="#tomkins_consciousness_1962">Tomkins 1962</a>. | ||
| 1758 | </div> | ||
| 1759 | </li><br><li class="footnote"> | ||
| 1760 | <div class="footnote2" id="fn27" href="#fna27">[<a href="#fna27">27</a>] | ||
| 1761 | </div> | ||
| 1762 | <div class="footnote3"><a title="Paul Ekman / Richard E. Sorenson / Wallace V. Friesen: Pan-cultural elements in facial displays of emotion. In: Science 164 (1969), i. 3875, pp. 86–88." href="#ekman_elements_1969">Ekman et al. 1969</a>, | ||
| 1763 | pp. 86–88. | ||
| 1764 | </div> | ||
| 1765 | </li><br><li class="footnote"> | ||
| 1766 | <div class="footnote2" id="fn28" href="#fna28">[<a href="#fna28">28</a>] | ||
| 1767 | </div> | ||
| 1320 | <div class="footnote3"><a title="Paul Ekman: Facial expression and emotion. In: American psychologist 48 (1993), i. 4, pp. 384–392." href="#ekman_expression_1993">Ekman 1993</a>, p. 386. | 1768 | <div class="footnote3"><a title="Paul Ekman: Facial expression and emotion. In: American psychologist 48 (1993), i. 4, pp. 384–392." href="#ekman_expression_1993">Ekman 1993</a>, p. 386. |
| 1322 | </li><br><li class="footnote"> | 1770 | </li><br><li class="footnote"> |
| 1323 | <div class="footnote2" id="fn27" href="#fna27">[<a href="#fna27">27</a>] | ||
| 1324 | </div> | ||
| 1325 | <div class="footnote3"><a title="Lisa Feldman Barrett: Discrete emotions or dimensions? The role of valence focus and arousal focus. In: Cognition & Emotion 12 (1998), i. 4, pp. 579–599." href="#feldman_emotions_1998">Feldman Barrett 1998</a>, pp. 580f. | ||
| 1326 | </div> | ||
| 1327 | </li><br><li class="footnote"> | ||
| 1328 | <div class="footnote2" id="fn28" href="#fna28">[<a href="#fna28">28</a>] | ||
| 1329 | </div> | ||
| 1330 | <div class="footnote3"><a title="James A. Russell: Is there universal recognition of emotion from facial expression? A review of the cross-cultural studies. In: Psychological bulletin 115 (1994), i. 1, pp. 102–141." href="#russel_recognition_1994">Russell 1994</a>; | ||
| 1331 | <a title="James A. Russell / Jo-Anne Bachorowski / José-Miguel Fernández-Dols: Facial and vocal expressions of emotion. In: Annual review of psychology 54 (2003), i. 1, pp. 329–349." href="#russel_expressions_2003">Russell et al. 2003</a>; | ||
| 1332 | <a title="Maria Gendron / Debi Roberso / Jacoba Marietta van der Vyver / Lisa Feldman Barrett: Perceptions of emotion from facial expressions are not culturally universal: Evidence from a remote culture. In: Emotion 14 (2014), i. 2, pp. 251–262." href="#gendron_emotion_2014">Gendron et al. 2014</a>; | ||
| 1333 | <a title="Lisa Feldman Barrett: How emotions are made: The secret life of the brain. Boston et al. 2017." href="#feldman_emotions_2017">Feldman Barrett 2017</a>. | ||
| 1334 | </div> | ||
| 1335 | </li><br><li class="footnote"> | ||
| 1336 | <div class="footnote2" id="fn29" href="#fna29">[<a href="#fna29">29</a>] | 1771 | <div class="footnote2" id="fn29" href="#fna29">[<a href="#fna29">29</a>] |
| 1337 | </div> | 1772 | </div> |
| 1338 | <div class="footnote3"><a title="Robert Plutchik: The Emotions. Revided edition. Lanham et al. 1991." href="#plutchik_emotions_1991">Plutchik 1991</a>, passim. | 1773 | <div class="footnote3"><a title="James A. Russell: Is there universal recognition of emotion from facial expression? A review of the cross-cultural studies. In: Psychological bulletin 115 (1994), i. 1, pp. 102–141." href="#russell_recognition_1994">Russell |
| 1339 | </div> | 1774 | 1994</a>; <a title="James A. Russell / Jo-Anne Bachorowski / José-Miguel Fernández-Dols: Facial and vocal expressions of emotion. In: Annual review of psychology 54 (2003), i. 1, pp. 329–349." href="#russell_expressions_2003">Russell et al. 2003</a>; <a title="Maria Gendron / Debi Roberso / Jacoba Marietta van der Vyver / Lisa Feldman Barrett: Perceptions of emotion from facial expressions are not culturally universal: Evidence from a remote culture. In: Emotion 14 (2014), i. 2, pp. 251–262." href="#gendron_emotion_2014">Gendron et al. 2014</a>; <a title="Lisa Feldman Barrett: How emotions are made: The secret life of the brain. Boston et al. 2017." href="#feldman_emotions_2017">Feldman Barrett |
| 1775 | 2017.</a></div> | ||
| 1340 | </li><br><li class="footnote"> | 1776 | </li><br><li class="footnote"> |
| 1342 | </div> | 1778 | </div> |
| 1343 | <div class="footnote3"><a title="Erik Cambria / Andrew Livingstone / Amir Hussain: The hourglass of emotions. In: Cognitive behavioural systems. Ed. by Anna Esposito et al. (COST 2102, Dresden, 21.-26.02.2011) Berlin 2012, pp. 144–157." href="#cambria_hourglass_2012">Cambria et al. 2012</a>; | 1779 | <div class="footnote3"><a title="Robert Plutchik: The Emotions. Revided edition. Lanham et al. 1991." href="#plutchik_emotions_1991">Plutchik 1991</a>. |
| 1344 | <a title="Suin Kim / JinYeong Bak / Alice Haeyun Oh: Do you feel what I feel? Social aspects of emotions in twitter conversations. In: Proceedings of the Sixth International AAAI Conference on Weblogs and Social Media. (ICWSM: 6, Dublin 04.-07.12.2012) Palo Alto, CA 2012, pp. 495–498." href="#kim_aspects_2012">Kim et al. 2012</a>; <a title="Jared Suttles / Nancy Ide: Distant supervision for emotion classification with discrete binary values. In: Computational Linguistics and Intelligent Text Processing. Ed. by Alexander Gelbukh. 2 volumes. (CICLing: 14, Samos, 24.-30.03.2013) Berlin et al. 2013. Vol. 2, pp. 121–136." href="#suttles_supervision_2013">Suttles / Ide 2013</a>; | ||
| 1345 | <a title="Damian Borth / Rongrong Ji / Tao Chen / Thomas Breuel / Shih-Fu Chang: Large-scale visual sentiment ontology and detectors using adjective noun pairs. In: Proceedings of the 21st ACM International Conference on Multimedia. (MM '13: 21, Barcelona, 21.-25.10.2013) New York, NY 2013, pp. 223–232." href="#borth_ontology_2013">Borth et al. 2013</a>; <a title="Muhammad Abdul-Mageed / Lyle Ungar: EmoNet: Fine-grained emotion detection with gated recurrent neural networks. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics. (ACL: 55, Vancouver, 30.07.-04.08.2017) New York, NY 2017, i 1, pp. 718–728. DOI: 10.18653/v1/P17-1067" href="#abdul_emotion_2017">Abdul-Mageed / | ||
| 1346 | Ungar 2017</a>. | ||
| 1347 | </div> | 1780 | </div> |
| 1350 | </div> | 1783 | </div> |
| 1351 | <div class="footnote3"><a title="Herman Smith / Andreas Schneider: Critiquing models of emotions. In: Sociological Methods & Research 37 (2009), i. 4, pp. 560–589." href="#smith_models_2009">Smith / Schneider 2009</a>, passim. | 1784 | <div class="footnote3"><a title="Erik Cambria / Andrew Livingstone / Amir Hussain: The hourglass of emotions. In: Cognitive behavioural systems. Ed. by Anna Esposito et al. (COST 2102, Dresden, 21.–26.02.2011) Berlin 2012, pp. 144–157." href="#cambria_hourglass_2012">Cambria et al. |
| 1785 | 2012</a>; <a title="Suin Kim / JinYeong Bak / Alice Haeyun Oh: Do you feel what I feel? Social aspects of emotions in twitter conversations. In: Proceedings of the Sixth International AAAI Conference on Weblogs and Social Media. (ICWSM: 6, Dublin 04.-07.12.2012) Palo Alto, CA 2012, pp. 495–498." href="#kim_aspects_2012">Kim | ||
| 1786 | et al. 2012</a>; <a title="Jared Suttles / Nancy Ide: Distant supervision for emotion classification with discrete binary values. In: Computational Linguistics and Intelligent Text Processing. Ed. by Alexander Gelbukh. 2 volumes. (CICLing: 14, Samos, 24.–30.03.2013) Berlin et al. 2013. Vol. 2, pp. 121–136." href="#suttles_supervision_2013">Suttles / Ide 2013</a>; <a title="Damian Borth / Rongrong Ji / Tao Chen / Thomas Breuel / Shih-Fu Chang: Large-scale visual sentiment ontology and detectors using adjective noun pairs. In: Proceedings of the 21st ACM International Conference on Multimedia. (MM '13: 21, Barcelona, 21.–25.10.2013) New York, NY 2013, pp. 223–232." href="#borth_ontology_2013">Borth et al. | ||
| 1787 | 2013</a>; <a title="Muhammad Abdul-Mageed / Lyle Ungar: EmoNet: Fine-grained emotion detection with gated recurrent neural networks. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics. (ACL: 55, Vancouver, 30.07.–04.08.2017) New York, NY 2017, i 1, pp. 718–728. DOI: 10.18653/v1/P17-1067" href="#abdul_emotion_2017">Abdul-Mageed / Ungar 2017</a>. | ||
| 1352 | </div> | 1788 | </div> |
| 1355 | </div> | 1791 | </div> |
| 1356 | <div class="footnote3"><a title="Marsha L. Richins: Measuring emotions in the consumption experience. In: Journal of consumer research 24 (1997), i. 2, pp. 127–146." href="#richins_emotions_1997">Richins 1997</a>, p. 128. | 1792 | <div class="footnote3"><a title="Herman Smith / Andreas Schneider: Critiquing models of emotions. In: Sociological Methods & Research 37 (2009), i. 4, pp. 560–589." href="#smith_models_2009">Smith / Schneider |
| 1793 | 2009</a>. | ||
| 1357 | </div> | 1794 | </div> |
| 1360 | </div> | 1797 | </div> |
| 1361 | <div class="footnote3"><a title="James A. Russell: A circumplex model of affect. In: Journal of Personality and Social Psychology 39 (1980), pp. 1161–1178." href="#russel_model_1980">Russell 1980</a>. | 1798 | <div class="footnote3"><a title="Marsha L. Richins: Measuring emotions in the consumption experience. In: Journal of consumer research 24 (1997), i. 2, pp. 127–146." href="#richins_emotions_1997">Richins 1997</a>, |
| 1799 | p. 128. | ||
| 1362 | </div> | 1800 | </div> |
| 1365 | </div> | 1803 | </div> |
| 1366 | <div class="footnote3"><a title=" | 1804 | <div class="footnote3"><a title="James A. Russell: A circumplex model of affect. In: Journal of Personality and Social Psychology 39 (1980), pp. 1161–1178." href="#russell_model_1980">Russell 1980</a>. |
| 1367 | </div> | 1805 | </div> |
| 1370 | </div> | 1808 | </div> |
| 1371 | <div class="footnote3"><a title="James A. Russell: Core affect and the psychological construction of emotion. In: Psychological review 110 (2003), i. 1, pp. 145–172." href="#russel_affect_2003">Russell 2003</a>, p. 154. | 1809 | <div class="footnote3"><a title="Margaret M. Bradley / Peter J. Lang: Measuring emotion: the self-assessment manikin and the semantic differential. In: Journal of behavior therapy and experimental psychiatry 25 (1994), i. 1, pp. 49–59." href="#bradley_emotion_1994">Bradley / Lang |
| 1810 | 1994</a>, p. 50. | ||
| 1372 | </div> | 1811 | </div> |
| 1375 | </div> | 1814 | </div> |
| 1376 | <div class="footnote3"><a title=" | 1815 | <div class="footnote3"><a title="James A. Russell: Core affect and the psychological construction of emotion. In: Psychological review 110 (2003), i. 1, pp. 145–172." href="#russell_affect_2003">Russell 2003</a>, p. 154. |
| 1377 | </div> | 1816 | </div> |
| 1380 | </div> | 1819 | </div> |
| 1381 | <div class="footnote3"><a title="James A. Russell / Lisa Feldman Barrett: Core affect, prototypical emotional episodes, and other things called emotion: dissecting the elephant. In: Journal of Personality and Social Psychology 76 (1999), i. 5, pp. 805–819." href="#russel_affect_1999">Russell / Feldman Barrett 1999</a>, p. 807. | 1820 | <div class="footnote3"><a title="Randy J. Larsen / Edward Diener: Promises and problems with the circumplex model of emotion. In: Emotion. Ed. by Margaret S. Clark. (= Review of personality and social psychology, 13) Newbury Park et al. 1992, pp. 25–29." href="#larsen_promises_1992">Larsen / Diener |
| 1821 | 1992</a>, p. | ||
| 1822 | 25. | ||
| 1382 | </div> | 1823 | </div> |
| 1385 | </div> | 1826 | </div> |
| 1386 | <div class="footnote3"><a title=" | 1827 | <div class="footnote3"><a title="James A. Russell / Lisa Feldman Barrett: Core affect, prototypical emotional episodes, and other things called emotion: dissecting the elephant. In: Journal of Personality and Social Psychology 76 (1999), i. 5, pp. 805–819." href="#russell_affect_1999">Russell / Feldman |
| 1387 | | 1828 | Barrett 1999</a>, p. 807. |
| 1388 | </div> | 1829 | </div> |
| 1391 | </div> | 1832 | </div> |
| 1392 | <div class="footnote3"><a title=" | 1833 | <div class="footnote3"><a title="Iris B. Mauss / Michael D. Robinson: Measures of emotion: A review. In: Cognition and Emotion 23 (2009), pp. 209–237. DOI: 10.1080/02699930802204677" href="#mauss_measures_2009">Mauss / Robinson 2009</a>. |
| 1393 | </div> | 1834 | </div> |
| 1396 | </div> | 1837 | </div> |
| 1397 | <div class="footnote3"><a title=" | 1838 | <div class="footnote3"><a title="Klaus R. Scherer: What are emotions? And how can they be measured? In: Social Science Information 44 (2005), i. 4, pp. 695–729." href="#scherer_emotions_2005">Scherer 2005</a>. |
| 1398 | </div> | 1839 | </div> |
| 1401 | </div> | 1842 | </div> |
| 1402 | <div class="footnote3"><a title=" | 1843 | <div class="footnote3"><a title="Craig. A. Smith / Phoebe C. Ellsworth: Patterns of cognitive appraisal in emotion. Journal of Personality and Social Psychology 48 (1985), pp. 813–838." href="#smith_patterns_1985">Smith / Ellsworth 1985</a>. |
| 1403 | </div> | 1844 | </div> |
| 1406 | </div> | 1847 | </div> |
| 1407 | <div class="footnote3"><a title="Leigh Van Horn: The characters within us: Readers connect with characters to create meaning and understanding. In: Journal of Adolescent & Adult Literacy 40 (1997), i. 5, pp. 342–347." href="#vanhorn_characters_1997">Van Horn | 1848 | <div class="footnote3"><a title="Lars Sætre / Patrizia Lombardo / Julien Zanetta (2014b): Text and Emotions. In: Exploring Text and Emotions. Ed. by Lars Sætre / Patrizia Lombardo / Julien Zanetta. Aarhus 2014, pp. 9–26." href="#saetre_text_2014">Sætre et al. 2014b</a>. |
| 1408 | 1997</a>, passim. | ||
| 1409 | </div> | 1849 | </div> |
| 1412 | </div> | 1852 | </div> |
| 1413 | <div class="footnote3"><a title=" | 1853 | <div class="footnote3"><a title="Jacques M. van Meel: Representing emotions in literature and paintings: a comparative analysis. In: Poetics 23 (1995), i. 1–2, pp. 159–176." href="#meel_emotions_1995">Van Meel 1995</a>. |
| 1414 | </div> | 1854 | </div> |
| 1417 | </div> | 1857 | </div> |
| 1418 | <div class="footnote3"><a title="Hillis J. Miller: Text; Action; Space; Emotion in Conrad’s Nostromo. In: Exploring Text and Emotions. Ed. by Lars Saetre / Lombardo / Julien Zanetta. Aarhus 2014, pp. 91–117." href="#miller_text_2014">Miller 2014</a>, p. 92. | 1858 | <div class="footnote3"><a title="Päivi Kuivalainen: Emotions in narrative: A linguistic study of Katherine Mansfield’s short fiction. In: The Electronic Journal of the Department of English at the University of Helsinki 5 (2009). [online]" href="#kuivalainen_emotions_2009">Kuivalainen |
| 1859 | 2009</a>. | ||
| 1419 | </div> | 1860 | </div> |
| 1422 | </div> | 1863 | </div> |
| 1423 | <div class="footnote3"><a title=" | 1864 | <div class="footnote3"><a title="James Barton: Interpreting character emotions for literature comprehension. In: Journal of Adolescent & Adult Literacy 40 (1996), i. 1, pp. 22–28." href="#barton_character_1996">Barton 1996</a>. |
| 1424 | </div> | 1865 | </div> |
| 1427 | </div> | 1868 | </div> |
| 1428 | <div class="footnote3"><a title="Hillis J. Miller: Text; Action; Space; Emotion in Conrad’s Nostromo. In: Exploring Text and Emotions. Ed. by Lars Saetre / Lombardo / Julien Zanetta. Aarhus 2014, pp. 91–117." href="#miller_text_2014">Miller 2014</a>, p. | 1869 | <div class="footnote3"><a title="Leigh Van Horn: The characters within us: Readers connect with characters to create meaning and understanding. In: Journal of Adolescent & Adult Literacy 40 (1997), i. 5, pp. 342–347." href="#vanhorn_characters_1997">Van Horn 1997</a>. |
| 1429 | 93. | ||
| 1430 | </div> | 1870 | </div> |
| 1433 | </div> | 1873 | </div> |
| 1434 | <div class="footnote3"><a title="Hillis J. Miller: Text; Action; Space; Emotion in Conrad’s Nostromo. In: Exploring Text and Emotions. Ed. by Lars Saetre / Lombardo / Julien Zanetta. Aarhus 2014, pp. 91–117." href="#miller_text_2014">Miller 2014</a>, p. 115. | 1874 | <div class="footnote3"><a title="Philip Nicholas Johnson-Laird / Keith Oatley: The language of emotions: An analysis of a semantic field. In: Cognition and emotion 3 (1989), i. 2, pp. 81–123." href="#johnson_language_1989">Johnson-Laird / Oatley |
| 1875 | 1989</a>. | ||
| 1435 | </div> | 1876 | </div> |
| 1438 | </div> | 1879 | </div> |
| 1880 | <div class="footnote3"><a title="Joseph Hillis Miller: Text; Action; Space; Emotion in Conrad’s Nostromo. In: Exploring Text and Emotions. Ed. by Lars Saetre / Lombardo / Julien Zanetta. Aarhus 2014, pp. 91–117." href="#miller_text_2014">Miller 2014</a>, p. 92. | ||
| 1881 | </div> | ||
| 1882 | </li><br><li class="footnote"> | ||
| 1883 | <div class="footnote2" id="fn49" href="#fna49">[<a href="#fna49">49</a>] | ||
| 1884 | </div> | ||
| 1885 | <div class="footnote3"><a title="Exploring Text and Emotions. Ed. by Lars Sætre / Patrizia Lombardo / Julien Zanetta (2014a). Aarhus 2014." href="#saetre_exploring_2014">Sætre et al. | ||
| 1886 | 2014a</a>, pp. 91ff. | ||
| 1887 | </div> | ||
| 1888 | </li><br><li class="footnote"> | ||
| 1889 | <div class="footnote2" id="fn50" href="#fna50">[<a href="#fna50">50</a>] | ||
| 1890 | </div> | ||
| 1891 | <div class="footnote3"><a title="Joseph Hillis Miller: Text; Action; Space; Emotion in Conrad’s Nostromo. In: Exploring Text and Emotions. Ed. by Lars Saetre / Lombardo / Julien Zanetta. Aarhus 2014, pp. 91–117." href="#miller_text_2014">Miller 2014</a>, p. 93. | ||
| 1892 | </div> | ||
| 1893 | </li><br><li class="footnote"> | ||
| 1894 | <div class="footnote2" id="fn51" href="#fna51">[<a href="#fna51">51</a>] | ||
| 1895 | </div> | ||
| 1896 | <div class="footnote3"><a title="Joseph Hillis Miller: Text; Action; Space; Emotion in Conrad’s Nostromo. In: Exploring Text and Emotions. Ed. by Lars Saetre / Lombardo / Julien Zanetta. Aarhus 2014, pp. 91–117." href="#miller_text_2014">Miller 2014</a>, p. 115. | ||
| 1897 | </div> | ||
| 1898 | </li><br><li class="footnote"> | ||
| 1899 | <div class="footnote2" id="fn52" href="#fna52">[<a href="#fna52">52</a>] | ||
| 1900 | </div> | ||
| 1901 | <div class="footnote3"> We recommend the essay by Katja Mellmann for further | ||
| 1902 | details on that topic. <a title="Katja Mellmann: E-Motion: Being Moved by Fiction and Media? Notes on Fictional Worlds, Virtual Contacts and the Reality of Emotions. In: PsyArt (2002). Article from 29.10.2002. [online]" href="#mellmann_emotion_2002">Mellmann 2002</a>. | ||
| 1903 | </div> | ||
| 1904 | </li><br><li class="footnote"> | ||
| 1905 | <div class="footnote2" id="fn53" href="#fna53">[<a href="#fna53">53</a>] | ||
| 1906 | </div> | ||
| 1439 | <div class="footnote3"><a title="Bing Liu: Sentiment Analysis: mining opinions, sentiments, and emotions. New York, NY 2015." href="#liu_opinions_2015">Liu 2015</a>, p. 47. | 1907 | <div class="footnote3"><a title="Bing Liu: Sentiment Analysis: mining opinions, sentiments, and emotions. New York, NY 2015." href="#liu_opinions_2015">Liu 2015</a>, p. 47. |
| 1441 | </li><br><li class="footnote"> | 1909 | </li><br><li class="footnote"> |
| 1442 | <div class="footnote2" id="fn49" href="#fna49">[<a href="#fna49">49</a>] | ||
| 1443 | </div> | ||
| 1444 | <div class="footnote3"><a title="Linda Barros / Pilar Rodriguez / Alvaro Ortigosa: Automatic classification of literature pieces by emotion detection: a study on quevedo’s poetry. In: 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction. (ACII 2013: 5, Geneva, 02.-05.09.2013), Piscataway, NJ 2013, pp. 141–146." href="#barros_classification_2013">Barros et al. 2013</a>, passim. | ||
| 1445 | </div> | ||
| 1446 | </li><br><li class="footnote"> | ||
| 1447 | <div class="footnote2" id="fn50" href="#fna50">[<a href="#fna50">50</a>] | ||
| 1448 | </div> | ||
| 1449 | <div class="footnote3"><a title="Ethan Reed: Measured unrest in the poetry of the black arts movement. Digital Humanities 2018: Puentes-Bridges. Book of Abstracts. Hg. von Jonathan Girón Palau / Isabel Galina Russell. (DH 2018, Mexico City, 26.-29.06.2018) Mexico City 2018, pp. 477–478. PDF. [online]" href="#reed_poetry_2018">Reed 2018</a>, passim. | ||
| 1450 | </div> | ||
| 1451 | </li><br><li class="footnote"> | ||
| 1452 | <div class="footnote2" id="fn51" href="#fna51">[<a href="#fna51">51</a>] | ||
| 1453 | </div> | ||
| 1454 | <div class="footnote3"><a title="Bei Yu: An evaluation of text classification methods for literary study. In: Literary and Linguistic Computing 23 (2008), i. 3, pp. 327–343. DOI: 10.1093/llc/fqn015" href="#yu_evaluation_2008">Yu 2008</a>, passim. | ||
| 1455 | </div> | ||
| 1456 | </li><br><li class="footnote"> | ||
| 1457 | <div class="footnote2" id="fn52" href="#fna52">[<a href="#fna52">52</a>] | ||
| 1458 | </div> | ||
| 1459 | <div class="footnote3"><a title="Albin Zehe / Martin Becker / Lena Hettinger / Andreas Hotho / Isabella Reger / Fotis Jannidis: Prediction of happy endings in German novels based on sentiment information. In: Proceedings of the Workshop on Interactions between Data Mining and Natural Language Processing 2016. Ed. by Peggy Cellier / Thierry Charnois / Andreas Hotho / Stan Matwin / Marie-Francine Moens / Yannick Toussaint. (DMNLP: 3, Riva del Garda, 19.-23.09.2016) Aachen 2016, pp. 9–16. URN: urn:nbn:de:0074-1646-4" href="#zehe_prediction_2016">Zehe et al. 2016</a>, passim. | ||
| 1460 | </div> | ||
| 1461 | </li><br><li class="footnote"> | ||
| 1462 | <div class="footnote2" id="fn53" href="#fna53">[<a href="#fna53">53</a>] | ||
| 1463 | </div> | ||
| 1464 | <div class="footnote3"><a title="Saif M. Mohammad / Peter D. Turney: Crowdsourcing a word–emotion association lexicon. In: Computational Intelligence 29 (2013), i. 3, pp. 436–465." href="#mohammad_crowdsourcing_2013">Mohammad / Turney 2013</a>, passim. | ||
| 1465 | </div> | ||
| 1466 | </li><br><li class="footnote"> | ||
| 1467 | <div class="footnote2" id="fn54" href="#fna54">[<a href="#fna54">54</a>] | 1910 | <div class="footnote2" id="fn54" href="#fna54">[<a href="#fna54">54</a>] |
| 1468 | </div> | 1911 | </div> |
| 1912 | <div class="footnote3"><a title="Linda Barros / Pilar Rodriguez / Alvaro Ortigosa: Automatic classification of literature pieces by emotion detection: a study on quevedo’s poetry. In: 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction. (ACII 2013: 5, Geneva, 02.-05.09.2013), Piscataway, NJ 2013, pp. 141–146." href="#barros_classification_2013">Barros | ||
| 1913 | et al. 2013</a>. | ||
| 1914 | </div> | ||
| 1915 | </li><br><li class="footnote"> | ||
| 1916 | <div class="footnote2" id="fn55" href="#fna55">[<a href="#fna55">55</a>] | ||
| 1917 | </div> | ||
| 1918 | <div class="footnote3"><a title="Ethan Reed: Measured unrest in the poetry of the black arts movement. Digital Humanities 2018: Puentes-Bridges. Book of Abstracts. Hg. von Jonathan Girón Palau / Isabel Galina Russell. (DH 2018, Mexico City, 26.–29.06.2018) Mexico City 2018, pp. 477–478. PDF. [online]" href="#reed_poetry_2018">Reed 2018</a></div> | ||
| 1919 | </li><br><li class="footnote"> | ||
| 1920 | <div class="footnote2" id="fn56" href="#fna56">[<a href="#fna56">56</a>] | ||
| 1921 | </div> | ||
| 1922 | <div class="footnote3"><a title="Bei Yu: An evaluation of text classification methods for literary study. In: Literary and Linguistic Computing 23 (2008), i. 3, pp. 327–343. DOI: 10.1093/llc/fqn015" href="#yu_evaluation_2008">Yu 2008</a>. | ||
| 1923 | </div> | ||
| 1924 | </li><br><li class="footnote"> | ||
| 1925 | <div class="footnote2" id="fn57" href="#fna57">[<a href="#fna57">57</a>] | ||
| 1926 | </div> | ||
| 1927 | <div class="footnote3"><a title="Ekaterina P. Volkova / Betty Mohler / Detmar Meurers / Dale Gerdemann / Heinrich H. Bülthoff: Emotional perception of fairy tales: achieving agreement in emotion annota-tion of text. In Proceedings of the NAACL HLT 2010 Workshop on Computational Ap-proaches to Analysis and Generation of Emotion in Text (2010), pp. 98–106. [online]" href="#volkova_perception_2010">Volkova et al. 2010</a>. | ||
| 1928 | </div> | ||
| 1929 | </li><br><li class="footnote"> | ||
| 1930 | <div class="footnote2" id="fn58" href="#fna58">[<a href="#fna58">58</a>] | ||
| 1931 | </div> | ||
| 1932 | <div class="footnote3"><a title="Vikas Ganjigunte Ashok / Song Feng / Yejin Choi: Success with Style: Using Writing Style to Predict the Success of Novels. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing. Ed. by Association for Computational Linguistics. (EMNLP, Seattle, WA, 18.–21.10.2013) Stroudsburg, PA 2013, pp. 1753–1764. [online]" href="#ashok_success_2013">Ashok et al. 2013?</a>. | ||
| 1933 | </div> | ||
| 1934 | </li><br><li class="footnote"> | ||
| 1935 | <div class="footnote2" id="fn59" href="#fna59">[<a href="#fna59">59</a>] | ||
| 1936 | </div> | ||
| 1937 | <div class="footnote3"><a title="Albin Zehe / Martin Becker / Lena Hettinger / Andreas Hotho / Isabella Reger / Fotis Jannidis: Prediction of happy endings in German novels based on sentiment information. In: Proceedings of the Workshop on Interactions between Data Mining and Natural Language Processing 2016. Ed. by Peggy Cellier / Thierry Charnois / Andreas Hotho / Stan Matwin / Marie-Francine Moens / Yannick Toussaint. (DMNLP: 3, Riva del Garda, 19.–23.09.2016) Aachen 2016, pp. 9–16. URN: urn:nbn:de:0074-1646-4" href="#zehe_prediction_2016">Zehe et al. | ||
| 1938 | 2016</a>. | ||
| 1939 | </div> | ||
| 1940 | </li><br><li class="footnote"> | ||
| 1941 | <div class="footnote2" id="fn60" href="#fna60">[<a href="#fna60">60</a>] | ||
| 1942 | </div> | ||
| 1943 | <div class="footnote3"><a title="Saif M. Mohammad / Peter D. Turney: Crowdsourcing a word–emotion association lexicon. In: Computational Intelligence 29 (2013), i. 3, pp. 436–465." href="#mohammad_crowdsourcing_2013">Mohammad / | ||
| 1944 | Turney 2013</a>. | ||
| 1945 | </div> | ||
| 1946 | </li><br><li class="footnote"> | ||
| 1947 | <div class="footnote2" id="fn61" href="#fna61">[<a href="#fna61">61</a>] | ||
| 1948 | </div> | ||
| 1469 | <div class="footnote3"><a title="Andrew J. Reagan / Lewis Mitchell / Dilan Kiley / Christopher M. Danforth / Peter Sheridan Dodds: The emotional arcs of stories are dominated by six basic shapes. In: EPJ Data Science 5 (2016), i. 1, pp. 31–43. DOI: 10.1140/epjds/s13688-016-0093-1" href="#reagan_arcs_2016">Reagan et al. | 1949 | <div class="footnote3"><a title="Andrew J. Reagan / Lewis Mitchell / Dilan Kiley / Christopher M. Danforth / Peter Sheridan Dodds: The emotional arcs of stories are dominated by six basic shapes. In: EPJ Data Science 5 (2016), i. 1, pp. 31–43. DOI: 10.1140/epjds/s13688-016-0093-1" href="#reagan_arcs_2016">Reagan et al. |
| 1470 | 2016</a>, passim. | 1950 | 2016</a>. |
| 1471 | </div> | ||
| 1472 | </li><br><li class="footnote"> | ||
| 1473 | <div class="footnote2" id="fn55" href="#fna55">[<a href="#fna55">55</a>] | ||
| 1474 | </div> | ||
| 1475 | <div class="footnote3"><a title="Kurt Vonnegut: Kurt Vonnegut at the Blackboard. Ed. by Seven Stories Press. New York, NY 2005. In: Lapham’s Quarterly (2010). Article from 26.03.2010. [online]" href="#vonnegut_blackboard_2010">Vonnegut 2010 (2005)</a>, passim. | ||
| 1476 | </div> | ||
| 1477 | </li><br><li class="footnote"> | ||
| 1478 | <div class="footnote2" id="fn56" href="#fna56">[<a href="#fna56">56</a>] | ||
| 1479 | </div> | ||
| 1480 | <div class="footnote3"><a title="Project Gutenberg. Ed. by Project Gutenberg Literary Archive Foundation. In: gutenberg.org. Salt Lake City, UT 1971-. [online]" href="#project_gutenberg_2019">Project Gutenberg 1971-2019</a>. | ||
| 1481 | </div> | ||
| 1482 | </li><br><li class="footnote"> | ||
| 1483 | <div class="footnote2" id="fn57" href="#fna57">[<a href="#fna57">57</a>] | ||
| 1484 | </div> | ||
| 1485 | <div class="footnote3"><a title="Spyridon Samothrakis / Maria Fasli: Emotional sentence annotation helps predict fiction genre. In: PLOS ONE 10 (2015), i. 11, p. e0141922. Article from 02.11.2015. DOI: 10.1371/journal.pone.0141922" href="#samothrakis_annotation_2015">Samothrakis / Fasli 2015</a>; | ||
| 1486 | <a title="Evgeny Kim / Sebastian Padó / Roman Klinger (2017a): Investigating the relationship between literary genres and emotional plot development. In: Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature - proceedings of the workshop. (SIGHUM, Vancouver, 04.08.2017) Stroudsburg, PA 2017, pp. 17–26. DOI: 10.18653/v1/W17-2203" href="#kim_relationship_2017">Kim et al. | ||
| 1487 | 2017a</a>; <a title="Evgeny Kim / Sebastian Padó / Roman Klinger (2017b): Prototypical emotion developments in adventures, romances, and mystery stories. In: Digital Humanities 2017: Conference Abstracts. Ed. by Rhian Lewis / Cecily Raynor / Dominic Forest / Michael Sinatra / Stéfan Sinclair. (DH 2017, Montreal, 08.-11.08.2017) Montreal 2017, pp. 288–291. PDF. [online]" href="#kim_emotion_2017">Kim et al. 2017b</a>. | ||
| 1488 | </div> | ||
| 1489 | </li><br><li class="footnote"> | ||
| 1490 | <div class="footnote2" id="fn58" href="#fna58">[<a href="#fna58">58</a>] | ||
| 1491 | </div> | ||
| 1492 | <div class="footnote3"><a title="Carlo Strapparava / Alessandro Valitutti. WordNet-Affect: An affective extension of WordNet. In: Proceedings of the 4th International Conference on Language Resources and Evaluation. Ed. by Maria Teresa Lino / Maria Francisca Xavier / Fátima Ferreira / Rute Costa / Raquel Silva. 9 volumes. (LREC: 4, Lisbon, 24.-30.05.2004) Paris et al. 2004. Vol. 4, pp. 1083–1086. PDF. [online]" href="#strapparava_extension_2004">Strapparava / Valitutti 2004</a>. | ||
| 1493 | </div> | ||
| 1494 | </li><br><li class="footnote"> | ||
| 1495 | <div class="footnote2" id="fn59" href="#fna59">[<a href="#fna59">59</a>] | ||
| 1496 | </div> | ||
| 1497 | <div class="footnote3"><a title="Evgeny Kim / Sebastian Padó / Roman Klinger (2017a): Investigating the relationship between literary genres and emotional plot development. In: Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature - proceedings of the workshop. (SIGHUM, Vancouver, 04.08.2017) Stroudsburg, PA 2017, pp. 17–26. DOI: 10.18653/v1/W17-2203" href="#kim_relationship_2017">Kim et al. 2017a</a>, passim. | ||
| 1498 | </div> | ||
| 1499 | </li><br><li class="footnote"> | ||
| 1500 | <div class="footnote2" id="fn60" href="#fna60">[<a href="#fna60">60</a>] | ||
| 1501 | </div> | ||
| 1502 | <div class="footnote3"><a title="Winthrop Nelson Francis / Henry Kucera: Brown corpus manual. Preface to revised Edition. Providence, RI 1979. [online]" href="#francis_corpus_1979">Francis / Kucera 1979</a>, passim. | ||
| 1503 | |||
| 1504 | </div> | ||
| 1505 | </li><br><li class="footnote"> | ||
| 1506 | <div class="footnote2" id="fn61" href="#fna61">[<a href="#fna61">61</a>] | ||
| 1507 | </div> | ||
| 1508 | <div class="footnote3"><a title="Ulrike Edith Gerda Henny-Krahmer: Exploration of sentiments and genre in Spanish American novels. In: Digital Humanities 2018: Puentes-Bridges. Book of Abstracts. Hg. von Jonathan Girón Palau / Isabel Galina Russell. (DH 2018, Mexico City, 26.-29.06.2018) Mexico City 2018, pp. 399–403. PDF. [online]" href="#henny_exploration_2018">Henny-Krahmer 2018</a>, passim. | ||
| 1509 | </div> | 1951 | </div> |
| 1512 | </div> | 1954 | </div> |
| 1513 | <div class="footnote3"><a title=" | 1955 | <div class="footnote3"><a title="Kurt Vonnegut: Kurt Vonnegut at the Blackboard. Ed. by Seven Stories Press. New York, NY 2005. In: Lapham’s Quarterly (2010). Article from 26.03.2010. [online]" href="#vonnegut_blackboard_2010">Vonnegut 2010 |
| 1514 | 1956 | (2005)</a>. | |
| 1515 | </div> | 1957 | </div> |
| 1518 | </div> | 1960 | </div> |
| 1519 | <div class="footnote3"><a title="Saif M. Mohammad / Peter D. Turney: Crowdsourcing a word–emotion association lexicon. In: Computational Intelligence 29 (2013), i. 3, pp. 436–465." href="#mohammad_crowdsourcing_2013">Mohammad / Turney 2013</a>. | 1961 | <div class="footnote3"><a title="Project Gutenberg. Ed. by Project Gutenberg Literary Archive Foundation. In: gutenberg.org. Salt Lake City, UT 1971–. https://www.gutenberg.org. [Webseite aus Deutschland nicht mehr erreichbar]" href="#project_gutenberg_2019">Project |
| 1962 | Gutenberg 1971–2019</a> [<i>Webseite aus Deutschland nicht mehr erreichbar</i>]. | ||
| 1520 | </div> | 1963 | </div> |
| 1523 | </div> | 1966 | </div> |
| 1524 | <div class="footnote3"><a title="Ryan Heuser / Franco Moretti / Erik Steiner: The emotions of London. Stanford 2016. (= Literary Lab Pamphlets, 13) PDF.[online]" href="#heuser_emotions_216">Heuser et al. 2016</a>, passim. | 1967 | <div class="footnote3"><a title="Spyridon Samothrakis / Maria Fasli: Emotional sentence annotation helps predict fiction genre. In: PLOS ONE 10 (2015), i. 11, p. e0141922. Article from 02.11.2015. DOI: 10.1371/journal.pone.0141922" href="#samothrakis_annotation_2015">Samothrakis / Fasli 2015</a>; <a title="Evgeny Kim / Sebastian Padó / Roman Klinger (2017a): Investigating the relationship between literary genres and emotional plot development. In: Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature – proceedings of the workshop. (SIGHUM, Vancouver, 04.08.2017) Stroudsburg, PA 2017, pp. 17–26. DOI: 10.18653/v1/W17-2203" href="#kim_relationship_2017">Kim et al. 2017a</a>; <a title="Evgeny Kim / Sebastian Padó / Roman Klinger (2017b): Prototypical emotion developments in adventures, romances, and mystery stories. In: Digital Humanities 2017: Conference Abstracts. Ed. by Rhian Lewis / Cecily Raynor / Dominic Forest / Michael Sinatra / Stéfan Sinclair. (DH 2017, Montreal, 08.–11.08.2017) Montreal 2017, pp. 288–291. PDF. [online]" href="#kim_emotion_2017">Kim et al. |
| 1968 | 2017b</a>. | ||
| 1525 | </div> | 1969 | </div> |
| 1528 | </div> | 1972 | </div> |
| 1529 | <div class="footnote3"><a title="Mapping emotions in Victorian London. Ed. by. Historypin. In: historypin.org. New Orleans et al. 2010-2017. [online]" href="#historypin_map_2017">Historypin 2010-2017</a>. | 1973 | <div class="footnote3"><a title="Carlo Strapparava / Alessandro Valitutti. WordNet-Affect: An affective extension of WordNet. In: Proceedings of the 4th International Conference on Language Resources and Evaluation. Ed. by Maria Teresa Lino / Maria Francisca Xavier / Fátima Ferreira / Rute Costa / Raquel Silva. 9 volumes. (LREC: 4, Lisbon, 24.–30.05.2004) Paris et al. 2004. Vol. 4, pp. 1083–1086. PDF. [online]" href="#strapparava_extension_2004">Strapparava |
| 1974 | / Valitutti 2004</a>. | ||
| 1530 | </div> | 1975 | </div> |
| 1533 | </div> | 1978 | </div> |
| 1534 | <div class="footnote3"><a title="André Bruggmann / Sara Irina Fabrikant: Spatializing a digital text archive about history. In: Workshop on Geographic Information Observatories 2014 : proceedings. Ed. by Krzysztof Janowicz / Benjamin Adams / Grant McKenzie / Tomi Kauppinen. (GIO 2014 / GIScience: 8, Vienna, 23.09.2014) Aachen 2014, pp. 6–14. (CEUR Workshop Proceedings, 1273) PDF. [online]" href="#bruggmann_text_2014">Bruggmann / Fabrikant 2014</a>, passim. | 1979 | <div class="footnote3"><a title="Evgeny Kim / Sebastian Padó / Roman Klinger (2017a): Investigating the relationship between literary genres and emotional plot development. In: Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature – proceedings of the workshop. (SIGHUM, Vancouver, 04.08.2017) Stroudsburg, PA 2017, pp. 17–26. DOI: 10.18653/v1/W17-2203" href="#kim_relationship_2017">Kim et al. |
| 1980 | 2017a</a>. | ||
| 1535 | </div> | 1981 | </div> |
| 1538 | </div> | 1984 | </div> |
| 1539 | <div class="footnote3"><a title="Philip J. Stone / Dexter C. Dunphy / Marshall S. Smith: The General Inquirer: A computer approach to content analysis. In: American Journal of Sociology 73 (1968), i. 5, pp. 634–635." href="#stone_inquirer_19688">Stone et al. 1968</a>. | 1985 | <div class="footnote3"><a title="Winthrop Nelson Francis / Henry Kucera: Brown corpus manual. Preface to revised Edition. Providence, RI 1979. [online]" href="#francis_corpus_1979">Francis / Kucera |
| 1986 | 1979</a>. | ||
| 1540 | </div> | 1987 | </div> |
| 1543 | </div> | 1990 | </div> |
| 1544 | <div class="footnote3"><a title="Maite Taboada / Mary Ann Gillies / Paul McFetridge: Sentiment classification techniques for tracking literary reputation. In: LREC workshop: Towards computational models of literary analysis. (LREC: 5, Genoa, 22.-28.05.2006) , pp. 36–43. Paris 2006. [online]" href="#taboada_classification_2006">Taboada et al. 2006</a>, passim; <a title="Maite Taboada / Mary Ann Gillies / Paul McFetridge / Robert Outtrim: Tracking literary reputation with text analysis tools. In: Meeting of the Society for Digital Humanities. Vancouver 2008. PDF. [online]" href="#taboada_reputation_2008">Taboada et al. 2008</a>, passim. | 1991 | <div class="footnote3"><a title="Ulrike Edith Gerda Henny-Krahmer: Exploration of sentiments and genre in Spanish American novels. In: Digital Humanities 2018: Puentes-Bridges. Book of Abstracts. Hg. von Jonathan Girón Palau / Isabel Galina Russell. (DH 2018, Mexico City, 26.–29.06.2018) Mexico City 2018, pp. 399–403. PDF. [online]" href="#henny_exploration_2018">Henny-Krahmer |
| 1992 | 2018</a>. | ||
| 1545 | </div> | 1993 | </div> |
| 1548 | </div> | 1996 | </div> |
| 1549 | <div class="footnote3"><a title="Annie T. Chen / Ayoung Yoon / Ryan Shaw: People, places and emotions: Visually representing historical context in oral testimonies. In: Proceedings of the Third Workshop on Computational Models of Narrative. (CMN’12: 3, Istanbul, 26.-27.05.2012), pp. 26–27. Cambridge, MA 2012. PDF. [online]" href="#chen_people_2012">Chen et al. 2012</a>, passim. | 1997 | <div class="footnote3"><a title="Stefano Baccianella / Andrea Esuli / Fabrizio Sebastiani: Sentiwordnet 3.0: An enhanced lexical resource for sentiment analysis and opinion mining. In: Proceedings of the 7th International Conference on Language Resources and Evaluation. (LREC’10: 7, Valetta, 17.05.–23.05.2010) Paris 2010, pp. 2200–2204. PDF. [online]" href="#baccianella_resource_2010">Baccianella |
| 1998 | et al. 2010</a>. | ||
| 1550 | </div> | 1999 | </div> |
| 1553 | </div> | 2002 | </div> |
| 1554 | <div class="footnote3"><a title="Carlo Strapparava / Alessandro Valitutti. WordNet-Affect: An affective extension of WordNet. In: Proceedings of the 4th International Conference on Language Resources and Evaluation. Ed. by Maria Teresa Lino / Maria Francisca Xavier / Fátima Ferreira / Rute Costa / Raquel Silva. 9 volumes. (LREC: 4, Lisbon, 24.-30.05.2004) Paris et al. 2004. Vol. 4, pp. 1083–1086. PDF. [online]" href="#strapparava_extension_2004"> | 2003 | <div class="footnote3"><a title="Saif M. Mohammad / Peter D. Turney: Crowdsourcing a word–emotion association lexicon. In: Computational Intelligence 29 (2013), i. 3, pp. 436–465." href="#mohammad_crowdsourcing_2013">Mohammad / |
| 2004 | Turney 2013</a>. | ||
| 2005 | </div> | ||
| 2006 | </li><br><li class="footnote"> | ||
| 2007 | <div class="footnote2" id="fn71" href="#fna71">[<a href="#fna71">71</a>] | ||
| 2008 | </div> | ||
| 2009 | <div class="footnote3"><a title="Ryan Heuser / Franco Moretti / Erik Steiner: The emotions of London. Stanford 2016. (= Literary Lab Pamphlets, 13) PDF.[online]" href="#heuser_emotions_216">Heuser et al. | ||
| 2010 | 2016</a>. | ||
| 2011 | </div> | ||
| 2012 | </li><br><li class="footnote"> | ||
| 2013 | <div class="footnote2" id="fn72" href="#fna72">[<a href="#fna72">72</a>] | ||
| 2014 | </div> | ||
| 2015 | <div class="footnote3"><a title="Mapping emotions in Victorian London. Ed. by Historypin. In: historypin.org. New Orleans et al. 2010–2017. [online]" href="#historypin_map_2017">Historypin | ||
| 2016 | 2010–2017</a>. | ||
| 2017 | </div> | ||
| 2018 | </li><br><li class="footnote"> | ||
| 2019 | <div class="footnote2" id="fn73" href="#fna73">[<a href="#fna73">73</a>] | ||
| 2020 | </div> | ||
| 2021 | <div class="footnote3"><a title="André Bruggmann / Sara Irina Fabrikant: Spatializing a digital text archive about history. In: Workshop on Geographic Information Observatories 2014 : proceedings. Ed. by Krzysztof Janowicz / Benjamin Adams / Grant McKenzie / Tomi Kauppinen. (GIO 2014 / GIScience: 8, Vienna, 23.09.2014) Aachen 2014, pp. 6–14. (CEUR Workshop Proceedings, 1273) PDF. [online]" href="#bruggmann_text_2014">Bruggmann / | ||
| 2022 | Fabrikant 2014</a>. | ||
| 2023 | </div> | ||
| 2024 | </li><br><li class="footnote"> | ||
| 2025 | <div class="footnote2" id="fn74" href="#fna74">[<a href="#fna74">74</a>] | ||
| 2026 | </div> | ||
| 2027 | <div class="footnote3"><a title="Philip J. Stone / Dexter C. Dunphy / Marshall S. Smith: The General Inquirer: A computer approach to content analysis. In: American Journal of Sociology 73 (1968), i. 5, pp. 634–635." href="#stone_inquirer_19688">Stone et al. | ||
| 2028 | 1968</a>. | ||
| 2029 | </div> | ||
| 2030 | </li><br><li class="footnote"> | ||
| 2031 | <div class="footnote2" id="fn75" href="#fna75">[<a href="#fna75">75</a>] | ||
| 2032 | </div> | ||
| 2033 | <div class="footnote3"><a title="Maite Taboada / Mary Ann Gillies / Paul McFetridge: Sentiment classification techniques for tracking literary reputation. In: LREC workshop: Towards computational models of literary analysis. (LREC: 5, Genoa, 22.-28.05.2006) , pp. 36–43. Paris 2006. [online]" href="#taboada_classification_2006">Taboada | ||
| 2034 | et al. 2006</a>; <a title="Maite Taboada / Mary Ann Gillies / Paul McFetridge / Robert Outtrim: Tracking literary reputation with text analysis tools. In: Meeting of the Society for Digital Humanities. Vancouver 2008. PDF. [online]" href="#taboada_reputation_2008">Taboada et al. 2008</a>. | ||
| 2035 | </div> | ||
| 2036 | </li><br><li class="footnote"> | ||
| 2037 | <div class="footnote2" id="fn76" href="#fna76">[<a href="#fna76">76</a>] | ||
| 2038 | </div> | ||
| 2039 | <div class="footnote3"><a title="Annie T. Chen / Ayoung Yoon / Ryan Shaw: People, places and emotions: Visually representing historical context in oral testimonies. In: Proceedings of the Third Workshop on Computational Models of Narrative. (CMN’12: 3, Istanbul, 26.–27.05.2012), pp. 26–27. Cambridge, MA 2012. PDF. [online]" href="#chen_people_2012">Chen et al. | ||
| 2040 | 2012</a>. | ||
| 2041 | </div> | ||
| 2042 | </li><br><li class="footnote"> | ||
| 2043 | <div class="footnote2" id="fn77" href="#fna77">[<a href="#fna77">77</a>] | ||
| 2044 | </div> | ||
| 2045 | <div class="footnote3"><a title="Carlo Strapparava / Alessandro Valitutti. WordNet-Affect: An affective extension of WordNet. In: Proceedings of the 4th International Conference on Language Resources and Evaluation. Ed. by Maria Teresa Lino / Maria Francisca Xavier / Fátima Ferreira / Rute Costa / Raquel Silva. 9 volumes. (LREC: 4, Lisbon, 24.–30.05.2004) Paris et al. 2004. Vol. 4, pp. 1083–1086. PDF. [online]" href="#strapparava_extension_2004"> | ||
| 1555 | Strapparava / Valitutti 2004</a>. | 2046 | Strapparava / Valitutti 2004</a>. |
| 1557 | </li><br><li class="footnote"> | 2048 | </li><br><li class="footnote"> |
| 1558 | <div class="footnote2" id="fn71" href="#fna71">[<a href="#fna71">71</a>] | ||
| 1559 | </div> | ||
| 1560 | <div class="footnote3"><a title="Oceanic Exchanges: Tracing Global Information Networks in Historical Newspaper Repositories, 1840-1914. Ed. by Oceanic Exchanges Project Team. Boston, MA 2017. [online]" href="#oceanic_global_2017">Oceanic Exchanges 2017</a>. | ||
| 1561 | </div> | ||
| 1562 | </li><br><li class="footnote"> | ||
| 1563 | <div class="footnote2" id="fn72" href="#fna72">[<a href="#fna72">72</a>] | ||
| 1564 | </div> | ||
| 1565 | <div class="footnote3"><a title="Alessandro Marchetti / Rachele Sprugnoli / Sara Tonelli: Sentiment analysis for the humanities: the case of historical texts. In: Digital Humanities 2014: Conference Abstracts. (DH 2014, Lausanne 08.-12.07.2014), Lausanne 2014, pp. 254–257. PDF. [online]" href="#marchetti_analysis_2014">Marchetti et al. 2014</a>, passim. | ||
| 1566 | </div> | ||
| 1567 | </li><br><li class="footnote"> | ||
| 1568 | <div class="footnote2" id="fn73" href="#fna73">[<a href="#fna73">73</a>] | ||
| 1569 | </div> | ||
| 1570 | <div class="footnote3"><a title="Rachele Sprugnoli / Sara Tonelli / Alessandro Marchetti / Giovanni Moretti: Towards sentiment analysis for historical texts. In: Digital Scholarship in the Humanities 31 (2016), i. 4, pp. 762–772. DOI: 10.1093/llc/fqv027" href="#sprugnoli_analysis_2016">Sprugnoli et al. 2016</a>, passim. | ||
| 1571 | </div> | ||
| 1572 | </li><br><li class="footnote"> | ||
| 1573 | <div class="footnote2" id="fn74" href="#fna74">[<a href="#fna74">74</a>] | ||
| 1574 | </div> | ||
| 1575 | <div class="footnote3"><a title="ALCIDE (Analysis of Language and Content In a Digital Environment). Demo. Ed. by Center for Information Technology Digital Humanities, Fondazione Bruno Kessler / Italian-German Historical Institute. In: fbk.eu. Alcide Demo. Trento 2014-2015. [online]" href="#alcide_cit_2014">ALCIDE Demo 2014-2015</a>. | ||
| 1576 | </div> | ||
| 1577 | </li><br><li class="footnote"> | ||
| 1578 | <div class="footnote2" id="fn75" href="#fna75">[<a href="#fna75">75</a>] | ||
| 1579 | </div> | ||
| 1580 | <div class="footnote3"><a title="Stefano Baccianella / Andrea Esuli / Fabrizio Sebastiani: Sentiwordnet 3.0: An enhanced lexical resource for sentiment analysis and opinion mining. In: Proceedings of the 7th International Conference on Language Resources and Evaluation. (LREC’10: 7, Valetta, 17.05.-23.05.2010) Paris 2010, pp. 2200–2204. PDF. [online]" href="#baccianella_resource_2010">Baccianella et al. 2010</a>, passim. | ||
| 1581 | </div> | ||
| 1582 | </li><br><li class="footnote"> | ||
| 1583 | <div class="footnote2" id="fn76" href="#fna76">[<a href="#fna76">76</a>] | ||
| 1584 | </div> | ||
| 1585 | <div class="footnote3"><a title="Emanuele Pianta / Luisa Bentivogli / Christian Girardi: MultiWordNet: Developing an aligned multilingual database. In: Proceedings of 1st International Global WordNet Conference. (GWC: 1, Mysore, 21.-25.02.2002) Mysore 2002, pp. 293–302. [online]" href="#pianta_database_2002">Pianta et al. 2002</a>, passim. | ||
| 1586 | </div> | ||
| 1587 | </li><br><li class="footnote"> | ||
| 1588 | <div class="footnote2" id="fn77" href="#fna77">[<a href="#fna77">77</a>] | ||
| 1589 | </div> | ||
| 1590 | <div class="footnote3"><a title="Sven Buechel / Johannes Hellrich / Udo Hahn: The course of emotion in three centuries of german text – a methodological framework. In: Digital Humanities 2017: Conference Abstracts. Ed. by Rhian Lewis et al. (DH 2017, Montreal, 08.-11.08.2017) Montreal 2017, pp. 176–179. [online]" href="#buechel_course_2017">Buechel et al. 2017</a>, passim. | ||
| 1591 | </div> | ||
| 1592 | </li><br><li class="footnote"> | ||
| 1593 | <div class="footnote2" id="fn78" href="#fna78">[<a href="#fna78">78</a>] | 2049 | <div class="footnote2" id="fn78" href="#fna78">[<a href="#fna78">78</a>] |
| 1594 | </div> | 2050 | </div> |
| 1595 | <div class="footnote3"><a title=" | 2051 | <div class="footnote3"><a title="Jianbo Gao / Matthew L. Jockers / John Laudun / Timothy Tangherlini: A multiscale theory for the dynamical evolution of sentiment in novels. In: International Conference on Behavioral, Economic and Socio-cultural Computing (BESC), 2016, pp. 1-4. DOI: 10.1109/BESC.2016.7804470" href="#gao_multiscale_2016">Geo et al. 2016</a>. |
| 1596 | </div> | 2052 | </div> |
| 1599 | </div> | 2055 | </div> |
| 1600 | <div class="footnote3"><a title="Deutsches Textarchiv. Grundlage für ein Referenzkorpus der neuhochdeutschen Sprache. Ed. by Berlin-Brandenburgischen Akademie der Wissenschaften. In: deutschestextarchiv.de. Berlin 2007-2019. [online]" href="#bbaw_dta_2019">Deutsches Textarchiv 2007-2019</a>. | 2056 | <div class="footnote3"><a title="Alessandro Marchetti / Rachele Sprugnoli / Sara Tonelli: Sentiment analysis for the humanities: the case of historical texts. In: Digital Humanities 2014: Conference Abstracts. (DH 2014, Lausanne 08.-12.07.2014), Lausanne 2014, pp. 254–257. PDF. [online]" href="#marchetti_analysis_2014">Marchetti |
| 2057 | et al. 2014</a>. | ||
| 1601 | </div> | 2058 | </div> |
| 1604 | </div> | 2061 | </div> |
| 1605 | <div class="footnote3"><a title="Inger Leemans / Janneke M. van der Zwaan / Isa Maks / Erika Kuijpers / Kristine Steenbergh: Mining embodied emotions: a comparative analysis of sentiment and emotion in dutch texts, 1600–1800. In: Digital Humanities Quaterly 11 (2017), i. 4. [online]" href="#leemans_emotions_2017">Leemans et al. 2017</a>, passim. | 2062 | <div class="footnote3"><a title="Rachele Sprugnoli / Sara Tonelli / Alessandro Marchetti / Giovanni Moretti: Towards sentiment analysis for historical texts. In: Digital Scholarship in the Humanities 31 (2016), i. 4, pp. 762–772. DOI: 10.1093/llc/fqv027" href="#sprugnoli_analysis_2016">Sprugnoli |
| 2063 | et al. 2016</a>. | ||
| 1606 | </div> | 2064 | </div> |
| 1609 | </div> | 2067 | </div> |
| 1610 | <div class="footnote3"><a title="James W. Pennebaker / Cindy K. Chung / Molly Ireland / Amy Gonzales / Roger J. Booth: The development and psychometric properties of LIWC2007. In: LIWC2007 Manual. liwc.net. 2007. PDF. [online]" href="#pennebaker_development_2007">Pennebaker et al. 2007</a>. | 2068 | <div class="footnote3"><a title="ALCIDE (Analysis of Language and Content In a Digital Environment). Demo. Ed. by Center for Information Technology Digital Humanities, Fondazione Bruno Kessler / Italian-German Historical Institute. In: fbk.eu. Alcide Demo. Trento 2014–2015. [online]" href="#alcide_cit_2014">ALCIDE Demo |
| 2069 | 2014–2015</a>. | ||
| 1611 | </div> | 2070 | </div> |
| 1614 | </div> | 2073 | </div> |
| 1615 | <div class="footnote3"><a title=" | 2074 | <div class="footnote3"><a title="Stefano Baccianella / Andrea Esuli / Fabrizio Sebastiani: Sentiwordnet 3.0: An enhanced lexical resource for sentiment analysis and opinion mining. In: Proceedings of the 7th International Conference on Language Resources and Evaluation. (LREC’10: 7, Valetta, 17.05.–23.05.2010) Paris 2010, pp. 2200–2204. PDF. [online]" href="#baccianella_resource_2010">Baccianella |
| 1616 | | 2075 | et al. 2010</a>. |
| 1617 | </div> | 2076 | </div> |
| 1620 | </div> | 2079 | </div> |
| 1621 | <div class="footnote3"><a title=" | 2080 | <div class="footnote3"><a title="Emanuele Pianta / Luisa Bentivogli / Christian Girardi: MultiWordNet: Developing an aligned multilingual database. In: Proceedings of 1st International Global WordNet Conference. (GWC: 1, Mysore, 21.–25.02.2002) Mysore 2002, pp. 293–302. [online]" href="#pianta_database_2002">Pianta et al. |
| 1622 | | 2081 | 2002</a>. |
| 1623 | </div> | 2082 | </div> |
| 1626 | </div> | 2085 | </div> |
| 1627 | <div class="footnote3"><a title="Eric T. Nalisnick / Henry S. Baird (2013a): Character-to-character sentiment analysis in shakespeare’s plays. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics. Ed. by Hinrich Schuetze / Pascale Fung / Massimo Poesio. 3 volumes. (ACL: 51, Sofia, 04.-09.08.2013) Red Hook, NY et al. 2013. Vol. 2: Short Papers, pp. 479–483. [online]" href="#nalisnick_analysis_2013">Nalisnick / Baird 2013a</a>, passim. | 2086 | <div class="footnote3"><a title="Sven Buechel / Johannes Hellrich / Udo Hahn: The course of emotion in three centuries of german text – a methodological framework. In: Digital Humanities 2017: Conference Abstracts. Ed. by Rhian Lewis et al. (DH 2017, Montreal, 08.–11.08.2017) Montreal 2017, pp. 176–179. [online]" href="#buechel_course_2017">Buechel et al. |
| 2087 | 2017</a>. | ||
| 1628 | </div> | 2088 | </div> |
| 1631 | </div> | 2091 | </div> |
| 1632 | <div class="footnote3"><a title="Finn Årup Nielsen: AFINN Sentiment Lexicon. In: corpustext.com. 2011. [online]" href="#nielsen_lexicon_2011">Nielsen 2011</a>, passim. | 2092 | <div class="footnote3"><a title="Sven Buechel / Johannes Hellrich / Udo Hahn: Feelings from the past – adapting affective lexicons for historical emotion analysis. In: Proceedings of the Workshop on Language Technology Resources and Tools for Digital Humanities. (LT4DH, Osaka, 11.12.2016) Stroudsburg, PA 2016, pp. 54–61. PDF. [online]" href="#buechel_feelings_2016">Buechel et al. |
| 2093 | 2016</a> p. | ||
| 2094 | 54, p. 59. | ||
| 1633 | </div> | 2095 | </div> |
| 1636 | </div> | 2098 | </div> |
| 1637 | <div class="footnote3"><a title="Eric T. Nalisnick / Henry S. Baird (2013b): Extracting sentiment networks from shakespeare’s plays. In: 12th International Conference on Document Analysis and Recognition. (ICDAR: 12, Washington, DC, 25.-28.08.2013) Piscataway, NJ 2013, pp. 758–762." href="#nalisnick_networs_2013">Nalisnick / Baird 2013b</a>, passim. | 2099 | <div class="footnote3"><a title="Deutsches Textarchiv. Grundlage für ein Referenzkorpus der neuhochdeutschen Sprache. Ed. by Berlin-Brandenburgischen Akademie der Wissenschaften. In: deutschestextarchiv.de. Berlin 2007–2019. [online]" href="#bbaw_dta_2019">Deutsches Textarchiv |
| 2100 | 2007–2019</a>. | ||
| 1638 | </div> | 2101 | </div> |
| 1641 | </div> | 2104 | </div> |
| 1642 | <div class="footnote3"><a title="Seth A. Marvel / Jon Kleinberg / Robert D. Kleinberg / Steven H. Strogatz: Continuous-time model of structural balance. In: Proceedings of the National Academy of Sciences 108 (2011), i. 5, pp. 1771–1776. DOI: 10.1073/pnas.1013213108" href="#marvel_model_2011">Marvel et al. 2011</a>. | 2105 | <div class="footnote3"><a title="Inger Leemans / Janneke M. van der Zwaan / Isa Maks / Erika Kuijpers / Kristine Steenbergh: Mining embodied emotions: a comparative analysis of sentiment and emotion in dutch texts, 1600–1800. In: Digital Humanities Quarterly 11 (2017), i. 4. [online]" href="#leemans_emotions_2017">Leemans et al. |
| 2106 | 2017</a>. | ||
| 1643 | </div> | 2107 | </div> |
| 1646 | </div> | 2110 | </div> |
| 1647 | <div class="footnote3"><a title=" | 2111 | <div class="footnote3"><a title="James W. Pennebaker / Cindy K. Chung / Molly Ireland / Amy Gonzales / Roger J. Booth: The development and psychometric properties of LIWC2007. In: LIWC2007 Manual. liwc.net. 2007. PDF. [online]" href="#pennebaker_development_2007">Pennebaker |
| 1648 | | 2112 | et al. 2007</a>. |
| 1649 | </div> | 2113 | </div> |
| 1652 | </div> | 2116 | </div> |
| 1653 | <div class="footnote3"><a title="Evgeny Kim / Roman Klinger: Who feels what and why? Annotation of a literature corpus with semantic roles of emotions. In: Proceedings of the 27th International Conference on Computational Linguistics. (COLING: 27, Santa Fe, NM, 20.-26.08.2018) Stroudsburg, PA 2018, pp. 1345–1359. PDF. [online]" href="#kim_annotation_2018">Kim / Klinger 2018</a>, passim. | 2117 | <div class="footnote3"><a title="Randy Ingermanson / Peter Economy. Writing fiction for dummies. Hoboken, NJ 2009." href="#ingermanson_fiction_2009">Ingermanson / |
| 2118 | Economy 2009</a>, p. 107. | ||
| 1654 | </div> | 2119 | </div> |
| 1657 | </div> | 2122 | </div> |
| 1658 | <div class="footnote3"><a title="REMAN - Relational Emotion Annotation for Fiction. Relational EMotion ANnotation – a corpus with 1720 fictional text exceprts from the Project Gutenberg. Ed. by Evgeny Kim / Roman Klinger, Universität Stuttgart, Institut für Maschinelle Sprachverarbeitung. In: ims.uni-stuttgart.de. Institut für Maschinelle Sprachverarbeitung. Forschung. Ressourcen Korpora. Stuttgart 2018. [online]" href="#reman_corpus_2019">REMAN - Relational Emotion Annotation for Fiction. Corpus 2018</a>. | 2123 | <div class="footnote3"><a title="Apoorv Agarwal / Anup Kotalwar / Owen Rambow: Automatic extraction of social networks from literary text: A case study on Alice in Wonderland. In: Proceedings of the Sixth International Joint Conference on Natural Language Processing. (IJCLP: 6, Nagoya 14.–18.10.2013) Nagoya 2013, pp. 1202–1208. [online]" href="#agarwal_extraction_2013">Agarwal et al. |
| 2124 | 2013</a>; <a title="David K. Elson / Nicholas Dames / Kathleen R. McKeown: Extracting social networks from literary fiction. In: Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics. (ACL: 48, Uppsala, 11.–18.07.2010) Red Hook, NY 2011, pp. 138–147. PDF. [online]" href="#elson_networks_2011">Elson | ||
| 2125 | et al. 2011</a>. | ||
| 1659 | </div> | 2126 | </div> |
| 1662 | </div> | 2129 | </div> |
| 1663 | <div class="footnote3"><a title="Florian Barth / Evgeny Kim / Sandra Murr / Roman Klinger: A reporting tool for relational visualization and analysis of character mentions in literature. In: DHd 2018: Kritik der digitalen Vernunft : Konferenzabstracts. Ed. by Georg Vogeler. (DHd 2018: 5, Köln, 26.02.-02.03.2018), Cologne 2018, pp. 123–127. PDF. [online]" href="#barth_tool_2018">Barth et al. 2018</a>, passim. | 2130 | <div class="footnote3"><a title="Eric T. Nalisnick / Henry S. Baird (2013a): Character-to-character sentiment analysis in shakespeare’s plays. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics. Ed. by Hinrich Schuetze / Pascale Fung / Massimo Poesio. 3 volumes. (ACL: 51, Sofia, 04.–09.08.2013) Red Hook, NY et al. 2013. Vol. 2: Short Papers, pp. 479–483. [online]" href="#nalisnick_analysis_2013">Nalisnick / |
| 2131 | Baird 2013a</a>. | ||
| 1664 | </div> | 2132 | </div> |
| 1667 | </div> | 2135 | </div> |
| 1668 | <div class="footnote3"><a title=" | 2136 | <div class="footnote3"><a title="Finn Årup Nielsen: AFINN Sentiment Lexicon. In: corpustext.com. 2011. [online]" href="#nielsen_lexicon_2011">Nielsen |
| 1669 | | 2137 | 2011</a>. |
| 1670 | </div> | 2138 | </div> |
| 1673 | </div> | 2141 | </div> |
| 1674 | <div class="footnote3"><a title="EMoFiel: Emotion Mapping of Fictional Relationship. Ed. by Harshita Jhavar / Paramita Mirza, Max Planck Institute for Informatics. In: mpi-inf.mpg.de. EMoFiel. Saarbrücken 2018. [online]" href="#emofiel_mpg_2018">EMoFiel: Emotion Mapping of Fictional Relationship 2018</a>. | 2142 | <div class="footnote3"><a title="Eric T. Nalisnick / Henry S. Baird (2013b): Extracting sentiment networks from shakespeare’s plays. In: 12th International Conference on Document Analysis and Recognition. (ICDAR: 12, Washington, DC, 25.–28.08.2013) Piscataway, NJ 2013, pp. 758–762." href="#nalisnick_networks_2013">Nalisnick / |
| 2143 | Baird 2013b</a>. | ||
| 1675 | </div> | 2144 | </div> |
| 1678 | </div> | 2147 | </div> |
| 1679 | <div class="footnote3"><a title="Robert Plutchik: The Emotions. Revided edition. Lanham et al. 1991." href="#plutchik_emotions_1991">Plutchik 1991</a>, passim. | 2148 | <div class="footnote3"><a title="Seth A. Marvel / Jon Kleinberg / Robert D. Kleinberg / Steven H. Strogatz: Continuous-time model of structural balance. In: Proceedings of the National Academy of Sciences 108 (2011), i. 5, pp. 1771–1776. DOI: 10.1073/pnas.1013213108" href="#marvel_model_2011">Marvel et al. |
| 2149 | 2011</a>. | ||
| 1680 | </div> | 2150 | </div> |
| 1683 | </div> | 2153 | </div> |
| 1684 | <div class="footnote3"><a title="James A. Russell: A circumplex model of affect. In: Journal of Personality and Social Psychology 39 (1980), pp. 1161–1178." href="#russel_model_1980">Russell 1980</a>, passim. | 2154 | <div class="footnote3"><a title="" href="#elsner_kernels_2012">Elsner 2012</a>; |
| 2155 | <a title="Micha Elsner: Abstract representations of plot structure. In: Linguistic Issues in Language Technology 12 (2015), i. 5. PDF. [online]" href="#elsner_representations_2015">Elsner 2015</a>. | ||
| 1685 | </div> | 2156 | </div> |
| 1688 | </div> | 2159 | </div> |
| 1689 | <div class="footnote3"><a title="Mattia Egloff / Antonio Lieto / Davide Picca: An ontological model for inferring psychological profiles and narrative roles of characters. In: Digital Humanities 2018: Puentes-Bridges. Book of Abstracts. Hg. von Jonathan Girón Palau / Isabel Galina Russell. (DH 2018, Mexico City, 26.-29.06.2018) Mexico City 2018, pp. 649–650. PDF. [online]" href="#egloff_model_2018">Egloff et al. 2018</a>, passim. | 2160 | <div class="footnote3"><a title="Evgeny Kim / Roman Klinger: Who feels what and why? Annotation of a literature corpus with semantic roles of emotions. In: Proceedings of the 27th International Conference on Computational Linguistics. (COLING: 27, Santa Fe, NM, 20.–26.08.2018) Stroudsburg, PA 2018, pp. 1345–1359. PDF. [online]" href="#kim_annotation_2018">Kim / Klinger |
| 2161 | 2018</a>. | ||
| 1690 | </div> | 2162 | </div> |
| 1693 | </div> | 2165 | </div> |
| 1694 | <div class="footnote3"><a title="Viviana Patti / Federico Bertola / Antonio Lieto: Arsemotica for arsmeteo.org: Emotion-driven exploration of online art collections. In: The Twenty-Eighth International Florida Artificial Intelligence Research Society Conference. Ed. by Ingrid Russell / William Eberle. (FLAIRS: 28, Hollywood, 18.-28.05.2015) Palo Alto, CA, pp. 288–293." href="#patti_explration_2015">Patti et al. 2015</a>. | 2166 | <div class="footnote3"><a title="REMAN - Relational Emotion Annotation for Fiction. Relational EMotion ANnotation – a corpus with 1720 fictional text exceprts from the Project Gutenberg. Ed. by Evgeny Kim / Roman Klinger, Universität Stuttgart, Institut für Maschinelle Sprachverarbeitung. In: ims.uni-stuttgart.de. Institut für Maschinelle Sprachverarbeitung. Forschung. Ressourcen Korpora. Stuttgart 2018. [online]" href="#reman_corpus_2019">REMAN – Relational |
| 2167 | Emotion Annotation for Fiction. Corpus 2018</a>. | ||
| 1695 | </div> | 2168 | </div> |
| 1698 | </div> | 2171 | </div> |
| 1699 | <div class="footnote3"><a title="Erik Cambria / Andrew Livingstone / Amir Hussain: The hourglass of emotions. In: Cognitive behavioural systems. Ed. by Anna Esposito et al. (COST 2102, Dresden, 21.-26.02.2011) Berlin 2012, pp. 144–157." href="#cambria_hourglass_2012">Cambria et al. 2012</a>, passim. | 2172 | <div class="footnote3"><a title="Harshita Jhavar / Paramita Mirza: EMOFIEL: Mapping emotions of relationships in a story. In: Companion Proceedings of the The Web Conference 2018. (WWW’18, Lyon, 23.–27.04.2018) Geneva 2018, pp. 243–246. DOI: 10.1145/3184558.3186989" href="#jhavar_emotions_2018">Jhavar / Mirza |
| 2173 | 2018</a>. | ||
| 1700 | </div> | 2174 | </div> |
| 1703 | </div> | 2177 | </div> |
| 1704 | <div class="footnote3"><a title="Evgeny Kim / Roman Klinger (2019b): Frowning Frodo, wincing Leia, and a seriously great friendship: Learning to classify emotional relationships of fictional characters. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Volume 1, Long and Short Papers. (NAACL-HLT, Minneapolis, MN, 02.-07.06.2019) Stroudsburg, PA 2019, pp. 647–653. DOI: 10.18653/v1/N19-1067" href="#kim_friendship_2019">Kim / Klinger 2019b</a>, passim. | 2178 | <div class="footnote3"><a title="EMoFiel: Emotion Mapping of Fictional Relationship. Ed. by Harshita Jhavar / Paramita Mirza, Max Planck Institute for Informatics. In: mpi-inf.mpg.de. EMoFiel. Saarbrücken 2018. [online]" href="#emofiel_mpg_2018">EMoFiel: Emotion |
| 2179 | Mapping of Fictional Relationship 2018</a>. | ||
| 1705 | </div> | 2180 | </div> |
| 1708 | </div> | 2183 | </div> |
| 2184 | <div class="footnote3"><a title="Robert Plutchik: The Emotions. Revided edition. Lanham et al. 1991." href="#plutchik_emotions_1991">Plutchik | ||
| 2185 | 1991</a>. | ||
| 2186 | </div> | ||
| 2187 | </li><br><li class="footnote"> | ||
| 2188 | <div class="footnote2" id="fn101" href="#fna101">[<a href="#fna101">101</a>] | ||
| 2189 | </div> | ||
| 2190 | <div class="footnote3"><a title="James A. Russell: A circumplex model of affect. In: Journal of Personality and Social Psychology 39 (1980), pp. 1161–1178." href="#russell_model_1980">Russell 1980</a>. | ||
| 2191 | </div> | ||
| 2192 | </li><br><li class="footnote"> | ||
| 2193 | <div class="footnote2" id="fn102" href="#fna102">[<a href="#fna102">102</a>] | ||
| 2194 | </div> | ||
| 2195 | <div class="footnote3"><a title="Mattia Egloff / Antonio Lieto / Davide Picca: An ontological model for inferring psychological profiles and narrative roles of characters. In: Digital Humanities 2018: Puentes-Bridges. Book of Abstracts. Hg. von Jonathan Girón Palau / Isabel Galina Russell. (DH 2018, Mexico City, 26.–29.06.2018) Mexico City 2018, pp. 649–650. PDF. [online]" href="#egloff_model_2018">Egloff et al. | ||
| 2196 | 2018</a>. | ||
| 2197 | </div> | ||
| 2198 | </li><br><li class="footnote"> | ||
| 2199 | <div class="footnote2" id="fn103" href="#fna103">[<a href="#fna103">103</a>] | ||
| 2200 | </div> | ||
| 2201 | <div class="footnote3"><a title="Viviana Patti / Federico Bertola / Antonio Lieto: Arsemotica for arsmeteo.org: Emotion-driven exploration of online art collections. In: The Twenty-Eighth International Florida Artificial Intelligence Research Society Conference. Ed. by Ingrid Russell / William Eberle. (FLAIRS: 28, Hollywood, 18.–28.05.2015) Palo Alto, CA, pp. 288–293." href="#patti_exploration_2015">Patti et al. | ||
| 2202 | 2015</a>. | ||
| 2203 | </div> | ||
| 2204 | </li><br><li class="footnote"> | ||
| 2205 | <div class="footnote2" id="fn104" href="#fna104">[<a href="#fna104">104</a>] | ||
| 2206 | </div> | ||
| 2207 | <div class="footnote3"><a title="Erik Cambria / Andrew Livingstone / Amir Hussain: The hourglass of emotions. In: Cognitive behavioural systems. Ed. by Anna Esposito et al. (COST 2102, Dresden, 21.–26.02.2011) Berlin 2012, pp. 144–157." href="#cambria_hourglass_2012">Cambria et al. | ||
| 2208 | 2012</a>. | ||
| 2209 | </div> | ||
| 2210 | </li><br><li class="footnote"> | ||
| 2211 | <div class="footnote2" id="fn105" href="#fna105">[<a href="#fna105">105</a>] | ||
| 2212 | </div> | ||
| 2213 | <div class="footnote3"><a title="Evgeny Kim / Roman Klinger (2019b): Frowning Frodo, wincing Leia, and a seriously great friendship: Learning to classify emotional relationships of fictional characters. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Volume 1, Long and Short Papers. (NAACL-HLT, Minneapolis, MN, 02.-07.06.2019) Stroudsburg, PA 2019, pp. 647–653. DOI: 10.18653/v1/N19-1067" href="#kim_friendship_2019">Kim / Klinger | ||
| 2214 | 2019b</a>. | ||
| 2215 | </div> | ||
| 2216 | </li><br><li class="footnote"> | ||
| 2217 | <div class="footnote2" id="fn106" href="#fna106">[<a href="#fna106">106</a>] | ||
| 2218 | </div> | ||
| 1709 | <div class="footnote3"><a title="Evgeny Kim / Roman Klinger (2019a): An analysis of emotion communication channels in fan-fiction: Towards emotional storytelling. In: Proceedings of the Second Workshop of Storytelling. Ed. by Francis Ferraro / Ting-Hao ›Kenneth‹ Huang / Stephanie M. Lukin / Margaret Mitchell. (Florence, 01.08.2019) Stroudsburg, PA 2019. DOI: 10.18653/v1/W19-3406" href="#kim_analysis_2019">Kim / Klinger | 2219 | <div class="footnote3"><a title="Evgeny Kim / Roman Klinger (2019a): An analysis of emotion communication channels in fan-fiction: Towards emotional storytelling. In: Proceedings of the Second Workshop of Storytelling. Ed. by Francis Ferraro / Ting-Hao ›Kenneth‹ Huang / Stephanie M. Lukin / Margaret Mitchell. (Florence, 01.08.2019) Stroudsburg, PA 2019. DOI: 10.18653/v1/W19-3406" href="#kim_analysis_2019">Kim / Klinger |
| 1710 | 2019a</a>, passim. | 2220 | 2019a</a>. |
| 1711 | </div> | ||
| 1712 | </li><br><li class="footnote"> | ||
| 1713 | <div class="footnote2" id="fn101" href="#fna101">[<a href="#fna101">101</a>] | ||
| 1714 | </div> | ||
| 1715 | <div class="footnote3"> Their | ||
| 1716 | analysis is based on <a title="Jacques M. van Meel: Representing emotions in literature and paintings: a comparative analysis. In: Poetics 23 (1995), i. 1–2, pp. 159–176." href="#meel_emotions_1995">Van Meel 1995</a> we mentioned in | ||
| 1717 | <a title="" href="#hd7">Section 3</a>. | ||
| 1718 | </div> | ||
| 1719 | </li><br><li class="footnote"> | ||
| 1720 | <div class="footnote2" id="fn102" href="#fna102">[<a href="#fna102">102</a>] | ||
| 1721 | </div> | ||
| 1722 | <div class="footnote3"><a title="Sergio Rinaldi / Pietro Landi / Fabio Della Rossa: Small discoveries can have great consequences in love affairs: the case of Beauty and the Beast. In: International Journal of Bifurcation and Chaos 23 (2013), i. 11." href="#rinaldi_discoveries_2013">Rinaldi et al. 2013</a>, passim. | ||
| 1723 | </div> | ||
| 1724 | </li><br><li class="footnote"> | ||
| 1725 | <div class="footnote2" id="fn103" href="#fna103">[<a href="#fna103">103</a>] | ||
| 1726 | </div> | ||
| 1727 | <div class="footnote3"><a title="Mikhail Zhuravlev / Irina Golovacheva / Polina de Mauny: Mathematical modelling of love affairs between the characters of the pre-masochistic novel. In: 2014 Second World Conference on Complex Systems (WCCS: 2, Adagir, 10.-12.11.2014) Piscataway, NJ 2014, pp. 396–401." href="#zhuravlev_affairs_2014">Zhuravlev et al. 2014</a>, passim. | ||
| 1728 | </div> | ||
| 1729 | </li><br><li class="footnote"> | ||
| 1730 | <div class="footnote2" id="fn104" href="#fna104">[<a href="#fna104">104</a>] | ||
| 1731 | </div> | ||
| 1732 | <div class="footnote3"><a title="Sajad Jafari / Julien Clinton Sprott / Seyed Mohammad Reza Hashemi Golpayegani: Layla and Majnun: A complex love story. In: Nonlinear Dynamics 83 (2016), i. 1, pp. 615–622." href="#jafri_story_2016">Jafari et al. 2016</a>, passim. | ||
| 1733 | </div> | ||
| 1734 | </li><br><li class="footnote"> | ||
| 1735 | <div class="footnote2" id="fn105" href="#fna105">[<a href="#fna105">105</a>] | ||
| 1736 | </div> | ||
| 1737 | <div class="footnote3"><a title="Clifford W. Anderson / George E. McMaster: Modeling emotional tone in stories using tension levels and categorical states. In: Computers and the Humanities 20 (1986), i. 1, pp. 3–9." href="#anderson_tone_1986"> Anderson / McMaster 1986</a>, passim. | ||
| 1738 | </div> | ||
| 1739 | </li><br><li class="footnote"> | ||
| 1740 | <div class="footnote2" id="fn106" href="#fna106">[<a href="#fna106">106</a>] | ||
| 1741 | </div> | ||
| 1742 | <div class="footnote3"><a title="David Reuben Jerome Heise: Semantic differential profiles for 1,000 most frequent English words. In: Psychological Monographs: General and Applied 79 (1965), i. 8, pp. 1–31." href="#heise_profiles_1965">Heise 1965</a>, passim. | ||
| 1743 | </div> | 2221 | </div> |
| 1746 | </div> | 2224 | </div> |
| 1747 | <div class="footnote3"><a title="Clifford W. Anderson / George E. McMaster: Computer assisted modeling of affective tone in written documents. In: Computers and the Humanities 16 (1982), i. 1, pp. 1–9." href="#anderson_computer_1982">Anderson / McMaster 1982</a>; | 2225 | <div class="footnote3"> Their analysis is based on <a title="Jacques M. van Meel: Representing emotions in literature and paintings: a comparative analysis. In: Poetics 23 (1995), i. 1–2, pp. 159–176." href="#meel_emotions_1995">Van Meel 1995</a> we mentioned in <a title="" href="#hd9">section 3</a>. |
| 1748 | <a title="Clifford W. Anderson / George E. McMaster: Emotional tone in Peter Rabbit before and after simplification. In: Empirical Studies of the Arts 11 (1993), i. 2, pp. 177–185." href="#anderson_tone_1993">Anderson / McMaster 1993</a>. | ||
| 1749 | </div> | 2226 | </div> |
| 1752 | </div> | 2229 | </div> |
| 1753 | <div class="footnote3"><a title="Cecilia Ovesdotter Alm / Richard Sproat: Emotional sequencing and development in fairy tales. In: Affective computing and intelligent interaction. First international conference. Proceedings. Ed. by Jianhua Tao et al. (ACII’05, Beijing, 22.-24.10.2005) Berlin et al. 2005, pp. 668–674." href="#alm_sequencing_2005">Alm / Sproat 2005</a>, passim. | 2230 | <div class="footnote3"><a title="Sergio Rinaldi / Pietro Landi / Fabio Della Rossa: Small discoveries can have great consequences in love affairs: the case of Beauty and the Beast. In: International Journal of Bifurcation and Chaos 23 (2013), i. 11." href="#rinaldi_discoveries_2013">Rinaldi |
| 2231 | et al. 2013</a>. | ||
| 1754 | </div> | 2232 | </div> |
| 1757 | </div> | 2235 | </div> |
| 1758 | <div class="footnote3"><a title="Saif M. Mohammad: From once upon a time to happily ever after: Tracking emotions in novels and fairy tales. In: Proceedings of the 5th ACL-HLT Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities. Ed. by Kalliopi Zervanou / Piroska Lendvai. (ACL-HT: 5, Portland, OR, 23.-24.06.2011) Stroudsburg, PA 2011, pp. 105–114. PDF. [online]" href="#mohammad_time_2011">Mohammad 2011</a>, passim; | 2236 | <div class="footnote3"><a title="Mikhail Zhuravlev / Irina Golovacheva / Polina de Mauny: Mathematical modelling of love affairs between the characters of the pre-masochistic novel. In: 2014 Second World Conference on Complex Systems (WCCS: 2, Adagir, 10.–12.11.2014) Piscataway, NJ 2014, pp. 396–401." href="#zhuravlev_affairs_2014">Zhuravlev |
| 1759 | <a title="Saif M. Mohammad: From once upon a time to happily ever after: Tracking emotions in mail and books. In: Decision Support Systems 53 (2012), i. 4, pp. 730–741." href="#mohammad_time_2012">Mohammad | 2237 | et al. 2014</a>. |
| 1760 | 2012</a>, passim. | ||
| 1761 | </div> | 2238 | </div> |
| 1764 | </div> | 2241 | </div> |
| 1765 | <div class="footnote3"><a title="Roman Klinger / Surayya Samat Suliya / Nils Reiter: Automatic Emotion Detection for Quantitative Literary Studies – A case study based on Franz Kafka’s “Das Schloss” and “Amerika”. In: Digital Humanities 2016: Conference Abstracts. Ed. by Maciej Eder / Jan Rybicki. (DH 2016, Kraków. 11.-16.07.2016) Kraków 2016, pp. 826–828. PDF. [online]" href="#klinger_emotion_2016">Klinger et al. 2016</a>, passim. | 2242 | <div class="footnote3"><a title="Sajad Jafari / Julien Clinton Sprott / Seyed Mohammad Reza Hashemi Golpayegani: Layla and Majnun: A complex love story. In: Nonlinear Dynamics 83 (2016), i. 1, pp. 615–622." href="#jafri_story_2016">Jafari et al. |
| 2243 | 2016</a>. | ||
| 1766 | </div> | 2244 | </div> |
| 1769 | </div> | 2247 | </div> |
| 1770 | <div class="footnote3"><a title="Evgeny Kim / Sebastian Padó / Roman Klinger (2017b): Prototypical emotion developments in adventures, romances, and mystery stories. In: Digital Humanities 2017: Conference Abstracts. Ed. by Rhian Lewis / Cecily Raynor / Dominic Forest / Michael Sinatra / Stéfan Sinclair. (DH 2017, Montreal, 08.-11.08.2017) Montreal 2017, pp. 288–291. PDF. [online]" href="#kim_emotion_2017">Kim et al. 2017b</a>, passim. | 2248 | <div class="footnote3"><a title="Clifford W. Anderson / George E. McMaster: Modeling emotional tone in stories using tension levels and categorical states. In: Computers and the Humanities 20 (1986), i. 1, pp. 3–9." href="#anderson_tone_1986"> Anderson / |
| 2249 | McMaster 1986</a>. | ||
| 1771 | </div> | 2250 | </div> |
| 1774 | </div> | 2253 | </div> |
| 1775 | <div class="footnote3"><a title=" | 2254 | <div class="footnote3"><a title="David Reuben Jerome Heise: Semantic differential profiles for 1,000 most frequent English words. In: Psychological Monographs: General and Applied 79 (1965), i. 8, pp. 1–31." href="#heise_profiles_1965">Heise 1965</a>. |
| 1776 | </div> | 2255 | </div> |
| 1779 | </div> | 2258 | </div> |
| 2259 | <div class="footnote3"><a title="Clifford W. Anderson / George E. McMaster: Computer assisted modeling of affective tone in written documents. In: Computers and the Humanities 16 (1982), i. 1, pp. 1–9." href="#anderson_computer_1982">Anderson / | ||
| 2260 | McMaster 1982</a>; <a title="Clifford W. Anderson / George E. McMaster: Emotional tone in Peter Rabbit before and after simplification. In: Empirical Studies of the Arts 11 (1993), i. 2, pp. 177–185." href="#anderson_tone_1993">Anderson / McMaster 1993</a>. | ||
| 2261 | </div> | ||
| 2262 | </li><br><li class="footnote"> | ||
| 2263 | <div class="footnote2" id="fn114" href="#fna114">[<a href="#fna114">114</a>] | ||
| 2264 | </div> | ||
| 2265 | <div class="footnote3"><a title="Cecilia Ovesdotter Alm / Richard Sproat: Emotional sequencing and development in fairy tales. In: Affective computing and intelligent interaction. First international conference. Proceedings. Ed. by Jianhua Tao et al. (ACII’05, Beijing, 22.-24.10.2005) Berlin et al. 2005, pp. 668–674." href="#alm_sequencing_2005">Alm / Sproat | ||
| 2266 | 2005</a>. | ||
| 2267 | </div> | ||
| 2268 | </li><br><li class="footnote"> | ||
| 2269 | <div class="footnote2" id="fn115" href="#fna115">[<a href="#fna115">115</a>] | ||
| 2270 | </div> | ||
| 2271 | <div class="footnote3"><a title="Saif M. Mohammad: From once upon a time to happily ever after: Tracking emotions in novels and fairy tales. In: Proceedings of the 5th ACL-HLT Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities. Ed. by Kalliopi Zervanou / Piroska Lendvai. (ACL-HT: 5, Portland, OR, 23.–24.06.2011) Stroudsburg, PA 2011, pp. 105–114. PDF. [online]" href="#mohammad_time_2011">Mohammad | ||
| 2272 | 2011</a>; <a title="Saif M. Mohammad: From once upon a time to happily ever after: Tracking emotions in mail and books. In: Decision Support Systems 53 (2012), i. 4, pp. 730–741." href="#mohammad_time_2012">Mohammad 2012</a>. | ||
| 2273 | </div> | ||
| 2274 | </li><br><li class="footnote"> | ||
| 2275 | <div class="footnote2" id="fn116" href="#fna116">[<a href="#fna116">116</a>] | ||
| 2276 | </div> | ||
| 2277 | <div class="footnote3"><a title="Roman Klinger / Surayya Samat Suliya / Nils Reiter: Automatic Emotion Detection for Quantitative Literary Studies – A case study based on Franz Kafka’s “Das Schloss” and “Amerika”. In: Digital Humanities 2016: Conference Abstracts. Ed. by Maciej Eder / Jan Rybicki. (DH 2016, Kraków. 11.–16.07.2016) Kraków 2016, pp. 826–828. PDF. [online]" href="#klinger_emotion_2016">Klinger et al. | ||
| 2278 | 2016</a>. | ||
| 2279 | </div> | ||
| 2280 | </li><br><li class="footnote"> | ||
| 2281 | <div class="footnote2" id="fn117" href="#fna117">[<a href="#fna117">117</a>] | ||
| 2282 | </div> | ||
| 2283 | <div class="footnote3"><a title="Thomas Schmidt / Manuel Burghardt: An Evaluation of Lexicon-based Sentiment Analysis Techniques for the Plays of Gotthold Ephraim Lessing. In: Proceedings of the Second Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature. Stroudsburg, PA 2018, pp. 139–149. [online]" href="#schmidt_evaluation_2018">Schmidt / Burghardt 2018</a>. | ||
| 2284 | </div> | ||
| 2285 | </li><br><li class="footnote"> | ||
| 2286 | <div class="footnote2" id="fn118" href="#fna118">[<a href="#fna118">118</a>] | ||
| 2287 | </div> | ||
| 2288 | <div class="footnote3"><a title="Evgeny Kim / Sebastian Padó / Roman Klinger (2017b): Prototypical emotion developments in adventures, romances, and mystery stories. In: Digital Humanities 2017: Conference Abstracts. Ed. by Rhian Lewis / Cecily Raynor / Dominic Forest / Michael Sinatra / Stéfan Sinclair. (DH 2017, Montreal, 08.–11.08.2017) Montreal 2017, pp. 288–291. PDF. [online]" href="#kim_emotion_2017">Kim et al. | ||
| 2289 | 2017b</a>. | ||
| 2290 | </div> | ||
| 2291 | </li><br><li class="footnote"> | ||
| 2292 | <div class="footnote2" id="fn119" href="#fna119">[<a href="#fna119">119</a>] | ||
| 2293 | </div> | ||
| 2294 | <div class="footnote3"><a title="Gustav Freytag: Die Technik des Dramas. Leipzig 1863." href="#freytag_technik_1863">Freytag | ||
| 2295 | 1863</a>. | ||
| 2296 | </div> | ||
| 2297 | </li><br><li class="footnote"> | ||
| 2298 | <div class="footnote2" id="fn120" href="#fna120">[<a href="#fna120">120</a>] | ||
| 2299 | </div> | ||
| 1780 | <div class="footnote3"><a title="Tuomo Kakkonen / Gordana Galic Kakkonen: Sentiprofiler: Creating comparable visual profiles of sentimental content in texts. In: Proceedings of the Workshop on Language Technologies for Digital Humanities and Cultural Heritage. Ed. by Cristina Vertan / Milena Slavcheva / Petya Osenova / Stelios Piperidis. (DigHum / RANLP: 8, Hissar, 16.09.2011) Shoumen 2011, pp. 62–69. PDF. [online]" href="#kakkonen_profiles_2011">Kakkonen / | 2300 | <div class="footnote3"><a title="Tuomo Kakkonen / Gordana Galic Kakkonen: Sentiprofiler: Creating comparable visual profiles of sentimental content in texts. In: Proceedings of the Workshop on Language Technologies for Digital Humanities and Cultural Heritage. Ed. by Cristina Vertan / Milena Slavcheva / Petya Osenova / Stelios Piperidis. (DigHum / RANLP: 8, Hissar, 16.09.2011) Shoumen 2011, pp. 62–69. PDF. [online]" href="#kakkonen_profiles_2011">Kakkonen / |
| 1781 | Galic Kakkonen 2011</a> | 2301 | Galic Kakkonen 2011</a>. |
| 1782 | </div> | 2302 | </div> |
| 1783 | </li><br><li class="footnote"> | 2303 | </li><br><li class="footnote"> |
| 1784 | <div class="footnote2" id=" | 2304 | <div class="footnote2" id="fn121" href="#fna121">[<a href="#fna121">121</a>] |
| 1785 | </div> | 2305 | </div> |
| 1786 | <div class="footnote3"><a title="Corina Koolen: Women’s books versus books by women. Digital Humanities 2018: Puentes-Bridges. Book of Abstracts. Hg. von Jonathan Girón Palau / Isabel Galina Russell. (DH 2018, Mexico City, 26. | 2306 | <div class="footnote3"><a title="Corina Koolen: Women’s books versus books by women. Digital Humanities 2018: Puentes-Bridges. Book of Abstracts. Hg. von Jonathan Girón Palau / Isabel Galina Russell. (DH 2018, Mexico City, 26.–29.06.2018) Mexico City 2018, pp. 219–222. PDF. [online]" href="#koolen_books_2018">Koolen 2018</a>. |
| 1787 | </div> | 2307 | </div> |
| 1788 | </li><br><li class="footnote"> | 2308 | </li><br><li class="footnote"> |
| 1789 | <div class="footnote2" id=" | 2309 | <div class="footnote2" id="fn122" href="#fna122">[<a href="#fna122">122</a>] |
| 1790 | </div> | 2310 | </div> |
| 1794 | </li><br><li class="footnote"> | 2314 | </li><br><li class="footnote"> |
| 1795 | <div class="footnote2" id="fn116" href="#fna116">[<a href="#fna116">116</a>] | ||
| 1796 | </div> | ||
| 1797 | <div class="footnote3"><a title="Eve Kraicer / Andrew Piper: Social characters: The hierarchy of gender in contemporary English-language fiction. In: Journal of Cultural Analytics (2019). Article from 30.01.2019. DOI: 10.22148/16.032" href="#kracier_characters_2019">Kraicer / Piper 2019</a>, passim. | ||
| 1798 | </div> | ||
| 1799 | </li><br><li class="footnote"> | ||
| 1800 | <div class="footnote2" id="fn117" href="#fna117">[<a href="#fna117">117</a>] | ||
| 1801 | </div> | ||
| 1802 | <div class="footnote3"><a title="Bing Liu: Sentiment analysis and subjectivity. In: Handbook of natural language processing. Ed. by Nitin Indurkhya / Fred Jacob Damerau. 2. edition. Boca Raton, FL 2010, pp. 627–666." href="#liu_analysis_2010">Liu et al. 2010</a>, passim. | ||
| 1803 | </div> | ||
| 1804 | </li><br><li class="footnote"> | ||
| 1805 | <div class="footnote2" id="fn118" href="#fna118">[<a href="#fna118">118</a>] | ||
| 1806 | </div> | ||
| 1807 | <div class="footnote3"><a title="Olivier Morin / Alberto Acerbi: Birth of the cool: a two-centuries decline in emotional expression in anglophone fiction. In: Cognition and Emotion 31 (2017), i. 8, pp. 1663–1675." href="#morin_birth_2017">Morin / Acerbi 2017</a>, passim. | ||
| 1808 | </div> | ||
| 1809 | </li><br><li class="footnote"> | ||
| 1810 | <div class="footnote2" id="fn119" href="#fna119">[<a href="#fna119">119</a>] | ||
| 1811 | </div> | ||
| 1812 | <div class="footnote3"><a title="Google Books Ngram Viewer. Ed. by Google. In: http://storage.googleapis.com. Version 2. 2012. [online]" href="#google_books_2012">Google Books Ngram Viewer 2012</a>. | ||
| 1813 | </div> | ||
| 1814 | </li><br><li class="footnote"> | ||
| 1815 | <div class="footnote2" id="fn120" href="#fna120">[<a href="#fna120">120</a>] | ||
| 1816 | </div> | ||
| 1817 | <div class="footnote3"><a title="James W. Pennebaker / Cindy K. Chung / Molly Ireland / Amy Gonzales / Roger J. Booth: The development and psychometric properties of LIWC2007. In: LIWC2007 Manual. liwc.net. 2007. PDF. [online]" href="#pennebaker_development_2007">Pennebaker et al. 2007</a>. | ||
| 1818 | </div> | ||
| 1819 | </li><br><li class="footnote"> | ||
| 1820 | <div class="footnote2" id="fn121" href="#fna121">[<a href="#fna121">121</a>] | ||
| 1821 | </div> | ||
| 1822 | <div class="footnote3"><a title="Alexander R. Bentley / Alberto Acerbi / Paul Ormerod / Vasileios Lampos: Books average previous decade of economic misery. In: PLOS ONE 9 (2014), i. 1, p. e83147. Article from 08.01.2014. DOI: 10.1371/journal.pone.0083147" href="#bentley_books_2014">Bentley et al. 2014</a>, passim. | ||
| 1823 | </div> | ||
| 1824 | </li><br><li class="footnote"> | ||
| 1825 | <div class="footnote2" id="fn122" href="#fna122">[<a href="#fna122">122</a>] | ||
| 1826 | </div> | ||
| 1827 | <div class="footnote3"><a title="Olivier Morin / Alberto Acerbi: Birth of the cool: a two-centuries decline in emotional expression in anglophone fiction. In: Cognition and Emotion 31 (2017), i. 8, pp. 1663–1675." href="#morin_birth_2017">Morin / Acerbi 2017</a>, passim. | ||
| 1828 | </div> | ||
| 1829 | </li><br><li class="footnote"> | ||
| 1830 | <div class="footnote2" id="fn123" href="#fna123">[<a href="#fna123">123</a>] | 2315 | <div class="footnote2" id="fn123" href="#fna123">[<a href="#fna123">123</a>] |
| 1831 | </div> | 2316 | </div> |
| 1832 | <div class="footnote3"><a title="Mika V. Mäntylä / Daniel Graziotin / Miikka Kuutila: The evolution of sentiment analysis – a review of research topics, venues, and top cited papers. In: Computer Science Review 27 (2018), pp. 16–32." href="#maentylae_evolution_2018">Mäntylä et al. 2018</a>, passim. | 2317 | <div class="footnote3"><a title="Eve Kraicer / Andrew Piper: Social characters: The hierarchy of gender in contemporary English-language fiction. In: Journal of Cultural Analytics (2019). Article from 30.01.2019. DOI: 10.22148/16.032" href="#kraicer_characters_2019">Kraicer / |
| 2318 | Piper 2019</a>. | ||
| 1833 | </div> | 2319 | </div> |
| 1836 | </div> | 2322 | </div> |
| 1837 | <div class="footnote3"><a title="Clifford W. Anderson / George E. McMaster: Computer assisted modeling of affective tone in written documents. In: Computers and the Humanities 16 (1982), i. 1, pp. 1–9." href="#anderson_computer_1982">Anderson / McMaster 1982</a>, passim. | 2323 | <div class="footnote3"><a title="Bing Liu: Sentiment analysis and subjectivity. In: Handbook of natural language processing. Ed. by Nitin Indurkhya / Fred Jacob Damerau. 2. edition. Boca Raton, FL 2010, pp. 627–666." href="#liu_analysis_2010">Liu et al. |
| 2324 | 2010</a>. | ||
| 1838 | </div> | 2325 | </div> |
| 1841 | </div> | 2328 | </div> |
| 1842 | <div class="footnote3"><a title="Ethan Reed: Measured unrest in the poetry of the black arts movement. Digital Humanities 2018: Puentes-Bridges. Book of Abstracts. Hg. von Jonathan Girón Palau / Isabel Galina Russell. (DH 2018, Mexico City, 26.-29.06.2018) Mexico City 2018, pp. 477–478. PDF. [online]" href="#reed_poetry_2018">Reed 2018</a>, passim. | 2329 | <div class="footnote3"><a title="Olivier Morin / Alberto Acerbi: Birth of the cool: a two-centuries decline in emotional expression in anglophone fiction. In: Cognition and Emotion 31 (2017), i. 8, pp. 1663–1675." href="#morin_birth_2017">Morin / Acerbi |
| 2330 | 2017</a>. | ||
| 1843 | </div> | 2331 | </div> |
| 1846 | </div> | 2334 | </div> |
| 1847 | <div class="footnote3"><a title=" | 2335 | <div class="footnote3"><a title="Google Books Ngram Viewer. Ed. by Google. In: http://storage.googleapis.com. Version 2. 2012. [online]" href="#google_books_2012">Google Books Ngram |
| 1848 | | 2336 | Viewer 2012</a>. |
| 1849 | </div> | 2337 | </div> |
| 1852 | </div> | 2340 | </div> |
| 1853 | <div class="footnote3"><a title=" | 2341 | <div class="footnote3"><a title="James W. Pennebaker / Cindy K. Chung / Molly Ireland / Amy Gonzales / Roger J. Booth: The development and psychometric properties of LIWC2007. In: LIWC2007 Manual. liwc.net. 2007. PDF. [online]" href="#pennebaker_development_2007">Pennebaker |
| 1854 | | 2342 | et al. 2007</a>. |
| 1855 | </div> | 2343 | </div> |
| 1858 | </div> | 2346 | </div> |
| 1859 | <div class="footnote3"><a title="Nan Z. Da: The computational case against computational literary studies. In: Critical Inquiry 45 (2019), i. 3, pp. 601–639." href="#da_case_2019">Da | 2347 | <div class="footnote3"><a title="Alexander R. Bentley / Alberto Acerbi / Paul Ormerod / Vasileios Lampos: Books average previous decade of economic misery. In: PLOS ONE 9 (2014), i. 1, p. e83147. Article from 08.01.2014. DOI: 10.1371/journal.pone.0083147" href="#bentley_books_2014">Bentley et al. |
| 1860 | 2019</a>, passim. | 2348 | 2014</a>. |
| 2349 | </div> | ||
| 2350 | </li><br><li class="footnote"> | ||
| 2351 | <div class="footnote2" id="fn129" href="#fna129">[<a href="#fna129">129</a>] | ||
| 2352 | </div> | ||
| 2353 | <div class="footnote3"><a title="Olivier Morin / Alberto Acerbi: Birth of the cool: a two-centuries decline in emotional expression in anglophone fiction. In: Cognition and Emotion 31 (2017), i. 8, pp. 1663–1675." href="#morin_birth_2017">Morin / Acerbi | ||
| 2354 | 2017</a>. | ||
| 2355 | </div> | ||
| 2356 | </li><br><li class="footnote"> | ||
| 2357 | <div class="footnote2" id="fn130" href="#fna130">[<a href="#fna130">130</a>] | ||
| 2358 | </div> | ||
| 2359 | <div class="footnote3"><a title="Mika V. Mäntylä / Daniel Graziotin / Miikka Kuutila: The evolution of sentiment analysis – a review of research topics, venues, and top cited papers. In: Computer Science Review 27 (2018), pp. 16–32." href="#maentylae_evolution_2018">Mäntylä et al. | ||
| 2360 | 2018</a>. | ||
| 2361 | </div> | ||
| 2362 | </li><br><li class="footnote"> | ||
| 2363 | <div class="footnote2" id="fn131" href="#fna131">[<a href="#fna131">131</a>] | ||
| 2364 | </div> | ||
| 2365 | <div class="footnote3"><a title="Clifford W. Anderson / George E. McMaster: Computer assisted modeling of affective tone in written documents. In: Computers and the Humanities 16 (1982), i. 1, pp. 1–9." href="#anderson_computer_1982">Anderson / McMaster | ||
| 2366 | 1982</a>. | ||
| 2367 | </div> | ||
| 2368 | </li><br><li class="footnote"> | ||
| 2369 | <div class="footnote2" id="fn132" href="#fna132">[<a href="#fna132">132</a>] | ||
| 2370 | </div> | ||
| 2371 | <div class="footnote3"><a title="Laura Ana Maria Oberländer / Kevin Reich / Roman Klinger: Experiencers, Stimuli, or Targets: Which Semantic Roles Enable Machine Learning to Infer the Emotions? In: Proceedings of the Third Workshop on Computational Modeling of People's Opinions, Personality, and Emotion's in Social Media. Barcelona 2020, pp. 119–128. [online]" href="#oberlaender_experiencers"> | ||
| 2372 | Oberländer et al. 2020</a>. | ||
| 1861 | </div> | 2373 | </div> |
| 1865 | <div class="bibliography"> | 2377 | <div class="bibliography"> |
| 1866 | <hr><a name=" | 2378 | <hr><a name="div31"> </a><div id="bibliography"><a name="hd29"> </a><h2> |
| 1867 | <div style="position:relative;width:90%;">Bibliographic References</div> | 2379 | <div style="position:relative;width:90%;">Bibliographic References</div> |
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| 2295 | pp. 396–401. [<a href="https://kxp.k10plus.de/DB=2.1/PPN?PPN=835708772" target="_blank">Nachweis im GVK</a>] | 2807 | </li><br><li class="biblio" id="schreibman_compainion_2016">Susan Schreibman / Ray Siemens / John |
| 2296 | </li><br><li class="biblio" id="zillmann_effect_1980">Dolf Zillmann / Richard T. Hezel / Norman J. Medoff: The effect of affective | 2808 | Unsworth: A New Companion to Digital Humanities. Chichester et al. 2015/2016. |
| 2297 | states on selective exposure to televised entertainment fare. In: Journal of Applied | 2809 | [<a href="https://kxp.k10plus.de/DB=2.1/PPN?PPN=838374875" target="_blank">Nachweis im GVK</a>] |
| 2298 | Social Psychology 10 (1980), i. 4, pp. 323–339. [<a href="https://kxp.k10plus.de/DB=2.1/PPN?PPN=129288942" target="_blank">Nachweis im GVK</a>] | 2810 | </li><br><li class="biblio" id="schwarz_emotion_2000">Norbert Schwarz: Emotion, cognition, and |
| 2811 | decision making. In: Cognition & Emotion 14 (2000), i. 4, pp. 433–440. [<a href="https://kxp.k10plus.de/DB=2.1/PPN?PPN=168132915" target="_blank">Nachweis im GVK</a>] | ||
| 2812 | </li><br><li class="biblio" id="shaikh_interpretation_2009">Mostafa Al Masum Shaikh / Helmut Prendinger / Mitsuru Ishizuka: A | ||
| 2813 | Linguistic Interpretation of the OCC Emotion Model for Affect Sensing | ||
| 2814 | from Text. In: Affective Information Processing. Ed. by Jianhua Tao / Tieniu Tan. | ||
| 2815 | London 2009. [<a href="https://kxp.k10plus.de/DB=2.1/PPN?PPN=562088369" target="_blank">Nachweis im GVK</a>] | ||
| 2816 | </li><br><li class="biblio" id="smith_patterns_1985">Craig. A. Smith / Phoebe C. Ellsworth: Patterns of cognitive | ||
| 2817 | appraisal in emotion. Journal of Personality and Social Psychology 48 | ||
| 2818 | (1985), pp. 813–838. [<a href="https://kxp.k10plus.de/DB=2.1/PPN?PPN=129078964" target="_blank">Nachweis im GVK</a>] | ||
| 2819 | </li><br><li class="biblio" id="smith_models_2009">Herman Smith / Andreas Schneider: Critiquing | ||
| 2820 | models of emotions. In: Sociological Methods & Research 37 (2009), i. 4, | ||
| 2821 | pp. 560–589. [<a href="https://kxp.k10plus.de/DB=2.1/PPN?PPN=129299553" target="_blank">Nachweis im GVK</a>] | ||
| 2822 | </li><br><li class="biblio" id="soleymani_survey_2017">Mohammad Soleymani / David Garcia / Brendan | ||
| 2823 | Jou / Björn Schuller / Shih-Fu Chang / Maja Pantic: A survey of multimodal | ||
| 2824 | sentiment analysis. In: Image and Vision Computing 65 (2017), pp. 3–14. [<a href="https://kxp.k10plus.de/DB=2.1/PPN?PPN=129144428" target="_blank">Nachweis im GVK</a>] | ||
| 2825 | </li><br><li class="biblio" id="sousa_emotion_2018">Ronald de Sousa / Andrea Scarantino: Emotion. | ||
| 2826 | In: The Stanford Encyclopedia of Philosophy. Ed. by Edward N. Zalta. Stanford, | ||
| 2827 | CA 2018. Article from 25.09.2018. [<a href="https://plato.stanford.edu/archives/win2018/entries/emotion/#Aca" target="_blank">online</a>] | ||
| 2828 | </li><br><li class="biblio" id="sprugnoli_analysis_2016">Rachele Sprugnoli / Sara Tonelli / | ||
| 2829 | Alessandro Marchetti / Giovanni Moretti: Towards sentiment analysis for | ||
| 2830 | historical texts. In: Digital Scholarship in the Humanities 31 (2016), i. 4, | ||
| 2831 | pp. 762–772. DOI: <a href="https://doi.org/10.1093/llc/fqv027" target="_blank">10.1093/llc/fqv027</a> [<a href="https://kxp.k10plus.de/DB=2.1/PPN?PPN=822236249" target="_blank">Nachweis im GVK</a>] | ||
| 2832 | </li><br><li class="biblio" id="stone_inquirer_19688">Philip J. Stone / Dexter C. Dunphy / | ||
| 2833 | Marshall S. Smith: The General Inquirer: A computer approach to content | ||
| 2834 | analysis. In: American Journal of Sociology 73 (1968), i. 5, pp. 634–635. [<a href="https://kxp.k10plus.de/DB=2.1/PPN?PPN=129077739" target="_blank">Nachweis im GVK</a>] | ||
| 2835 | </li><br><li class="biblio" id="strapparava_extension_2004">Carlo Strapparava / Alessandro | ||
| 2836 | Valitutti. WordNet-Affect: An affective extension of WordNet. In: Proceedings | ||
| 2837 | of the 4th International Conference on Language Resources and Evaluation. Ed. | ||
| 2838 | by Maria Teresa Lino / Maria Francisca Xavier / Fátima Ferreira / Rute Costa / | ||
| 2839 | Raquel Silva. 9 volumes. (LREC: 4, Lisbon, 24.–30.05.2004) Paris et al. 2004. | ||
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| 2841 | </li><br><li class="biblio" id="suttles_supervision_2013">Jared Suttles / Nancy Ide: Distant | ||
| 2842 | supervision for emotion classification with discrete binary values. In: | ||
| 2843 | Computational Linguistics and Intelligent Text Processing. Ed. by Alexander | ||
| 2844 | Gelbukh. 2 volumes. (CICLing: 14, Samos, 24.–30.03.2013) Berlin et al. 2013. | ||
| 2845 | Vol. 2, pp. 121–136. [<a href="https://kxp.k10plus.de/DB=2.1/PPN?PPN=744907373" target="_blank">Nachweis im GVK</a>] | ||
| 2846 | </li><br><li class="biblio" id="taboada_classification_2006">Maite Taboada / Mary Ann Gillies / Paul | ||
| 2847 | McFetridge: Sentiment classification techniques for tracking literary | ||
| 2848 | reputation. In: LREC workshop: Towards computational models of literary | ||
| 2849 | analysis. (LREC: 5, Genoa, 22.-28.05.2006) , pp. 36–43. Paris 2006. [<a href="http://www.lrec-conf.org/proceedings/lrec2006/workshops/W04/Literary%20analysis.pdf" target="_blank">online</a>] | ||
| 2850 | </li><br><li class="biblio" id="taboada_reputation_2008">Maite Taboada / Mary Ann Gillies / Paul | ||
| 2851 | McFetridge / Robert Outtrim: Tracking literary reputation with text analysis | ||
| 2852 | tools. In: Meeting of the Society for Digital Humanities. Vancouver 2008. PDF. | ||
| 2853 | [<a href="http://www.sfu.ca/~mtaboada/docs/publications/Taboada_et_al_SDH_2008.pdf" target="_blank">online</a>] | ||
| 2854 | </li><br><li class="biblio" id="tolstoy_art_1962">Leo Tolstoy: What is art? And essays on art. | ||
| 2855 | Harmondsworth 1962. (= Penguin classics) Siehe auch [<a href="https://kxp.k10plus.de/DB=2.1/PPN?PPN=21799721X" target="_blank">Nachweis im GVK</a>] | ||
| 2856 | </li><br><li class="biblio" id="tomkins_consciousness_1962">Silvan Tomkins: Affect imagery | ||
| 2857 | consciousness. 4 vol. New York, NY et al. 1962. Vol. I: The positive affects. | ||
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| 2859 | </li><br><li class="biblio" id="vanhorn_characters_1997">Leigh Van Horn: The characters within us: | ||
| 2860 | Readers connect with characters to create meaning and understanding. In: | ||
| 2861 | Journal of Adolescent & Adult Literacy 40 (1997), i. 5, pp. 342–347. [<a href="https://kxp.k10plus.de/DB=2.1/PPN?PPN=19105335X" target="_blank">Nachweis im GVK</a>] | ||
| 2862 | </li><br><li class="biblio" id="volkova_perception_2010">Ekaterina P. Volkova / Betty Mohler / Detmar Meurers / Dale Gerdemann | ||
| 2863 | / Heinrich H. Bülthoff: Emotional perception of fairy tales: achieving | ||
| 2864 | agreement in emotion annota-tion of text. In Proceedings of the NAACL | ||
| 2865 | HLT 2010 Workshop on Computational Ap-proaches to Analysis and | ||
| 2866 | Generation of Emotion in Text (2010), pp. 98–106. [<a href="https://www.aclweb.org/anthology/W10-0212" target="_blank">online</a>] | ||
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| 2868 | and definition of digital|humanities|computing. In: Defining Digital | ||
| 2869 | Humanities. A Reader. Ed. by Meliss Terras / Julianne Hyhan / Edward Vanhoutte. | ||
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| 2871 | </li><br><li class="biblio" id="vonnegut_blackboard_2010">Kurt Vonnegut: Kurt Vonnegut at the | ||
| 2872 | Blackboard. Ed. by Seven Stories Press. New York, NY 2005. In: Lapham’s | ||
| 2873 | Quarterly (2010). Article from 26.03.2010. [<a href="https://www.laphamsquarterly.org/arts-letters/blackboard" target="_blank">online</a>] | ||
| 2874 | </li><br><li class="biblio" id="wiebe_language_2004">Janyce Wiebe / Theresa Wilson / Rebecca Bruce / Matthew Bell / Melanie | ||
| 2875 | Martin: Learning Subjective Language. In: Computational Linguistics 30 | ||
| 2876 | (2004), pp. 277–308. [<a href="https://kxp.k10plus.de/DB=2.1/PPN?PPN=130396419" target="_blank">Nachweis im GVK</a>] | ||
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| 2878 | methods for literary study. In: Literary and Linguistic Computing 23 (2008), i. | ||
| 2879 | 3, pp. 327–343. DOI: <a href="https://doi.org/10.1093/llc/fqn015" target="_blank">10.1093/llc/fqn015</a></li><br><li class="biblio" id="zehe_prediction_2016">Albin Zehe / Martin Becker / Lena Hettinger / | ||
| 2880 | Andreas Hotho / Isabella Reger / Fotis Jannidis: Prediction of happy endings in | ||
| 2881 | German novels based on sentiment information. In: Proceedings of the Workshop | ||
| 2882 | on Interactions between Data Mining and Natural Language Processing 2016. Ed. | ||
| 2883 | by Peggy Cellier / Thierry Charnois / Andreas Hotho / Stan Matwin / | ||
| 2884 | Marie-Francine Moens / Yannick Toussaint. (DMNLP: 3, Riva del Garda, | ||
| 2885 | 19.–23.09.2016) Aachen 2016, pp. 9–16. URN: <a href="http://nbn-resolving.de/urn:nbn:de:0074-1646-4" target="_blank">urn:nbn:de:0074-1646-4</a></li><br><li class="biblio" id="zhuravlev_affairs_2014">Mikhail Zhuravlev / Irina Golovacheva / | ||
| 2886 | Polina de Mauny: Mathematical modelling of love affairs between the characters | ||
| 2887 | of the pre-masochistic novel. In: 2014 Second World Conference on Complex | ||
| 2888 | Systems (WCCS: 2, Adagir, 10.–12.11.2014) Piscataway, NJ 2014, pp. 396–401. | ||
| 2889 | [<a href="https://kxp.k10plus.de/DB=2.1/PPN?PPN=835708772" target="_blank">Nachweis im GVK</a>] | ||
| 2890 | </li><br><li class="biblio" id="zillmann_effect_1980">Dolf Zillmann / Richard T. Hezel / Norman J. | ||
| 2891 | Medoff: The effect of affective states on selective exposure to televised | ||
| 2892 | entertainment fare. In: Journal of Applied Social Psychology 10 (1980), i. 4, | ||
| 2893 | pp. 323–339. [<a href="https://kxp.k10plus.de/DB=2.1/PPN?PPN=129288942" target="_blank">Nachweis im GVK</a>] | ||
| 2299 | </li><br></ul> | 2894 | </li><br></ul> |
| 2300 | </div> | 2895 | </div> |
| 2896 | </div> | ||
| 2301 | <div class="abbildungsnachweis"> | 2897 | <div class="abbildungsnachweis"> |
| 2302 | <hr><a name=" | 2898 | <hr><a name="div32"> </a><div id="abbildungsnachweis"><a name="hd30"> </a><h2> |
| 2303 | <div style="position:relative;width:90%;">List of Figures with Captions</div> | 2899 | <div style="position:relative;width:90%;">List of Figures with Captions</div> |
| 2305 | <ul class="abbildung"> | 2901 | <ul class="abbildung"> |
| 2306 | <li id="abb1"><a href="#emotion_analysis_2019_001">Fig. 1</a>: Plutchik’s wheel of emotions. [<a href="#plutchik_wheel_2011">Plutchik 2011</a>. | 2902 | <li id="abb1"><a href="#emotion_analysis_2019_001">Fig. 1</a>: Plutchik’s wheel of emotions. [<a href="#plutchik_wheel_2011">Plutchik 2011</a>. <a href="https://creativecommons.org/publicdomain/mark/1.0/deed.de">PD</a>]<a href="#emotion_analysis_2019_001"></a></li> |
| 2307 | <a href="https://creativecommons.org/publicdomain/mark/1.0/deed.de">PD</a>]<a href="#emotion_analysis_2019_001"></a></li> | ||
| 2308 | </ul> | 2903 | </ul> |
| 2309 | <ul class="abbildung"> | 2904 | <ul class="abbildung"> |
| 2310 | <li id="abb2"><a href="#emotion_analysis_2019_002">Fig. 2</a>: Circumplex model of affect: Horizontal axis represents the valence dimension, | 2905 | <li id="abb2"><a href="#emotion_analysis_2019_002">Fig. 2</a>: Circumplex model of affect: Horizontal axis |
| 2311 | the vertical axis represents the arousal dimension. Drawn after <a href="#posner_model_2005">Posner et al. 2005</a>. [Kim / Klinger 2019]<a href="#emotion_analysis_2019_002"></a></li> | 2906 | represents the valence dimension, the vertical axis represents the arousal |
| 2907 | dimension. Drawn after <a href="#posner_model_2005">Posner | ||
| 2908 | et al. 2005</a>. [Kim / Klinger 2019]<a href="#emotion_analysis_2019_002"></a></li> | ||
| 2312 | </ul> | 2909 | </ul> |
| 2313 | <ul class="abbildung"> | 2910 | <ul class="abbildung"> |
| 2314 | <li id="abb3"><a href="#emotion_analysis_2019_003">Tab. 3</a>: Summary of characteristics of methods used in the papers reviewed | 2911 | <li id="abb3"><a href="#emotion_analysis_2019_003">Tab. 1</a>: Summary of characteristics of methods used in the |
| 2315 | in this survey. Download as <a href="http://www.zfdg.de/files/table_zfdg_klinger.pdf">PDF</a>. [Kim / Klinger 2019]<a href="#emotion_analysis_2019_003"></a></li> | 2912 | papers reviewed in this survey. Download as <a href="http://www.zfdg.de/files/table_zfdg_klinger.pdf">PDF</a>. [Kim / |
| 2913 | Klinger 2021]<a href="#emotion_analysis_2019_003"></a></li> | ||
| 2316 | </ul> | 2914 | </ul> |
| 2317 | </div> | 2915 | </div> |
| 2916 | </div> | ||
| 2917 | </body> | ||
| 2918 | |||
| 2919 | |||