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4	Evgeny Kim	3	Evgeny Kim
5	Kontakt: evgeny.kim@ims.uni-stuttgart.deInstitution: Universität Stuttgart, Institut für Maschinelle	4	Kontakt: evgeny.kim@ims.uni-stuttgart.deInstitution: Universität Stuttgart, Institut für Maschinelle Sprachverarbeitung GND: 1193672481ORCID: 0000-0001-6822-6709
6	Sprachverarbeitung GND: 1193672481ORCID: 0000-0001-6822-6709
7	Roman Klinger	5	Roman Klinger
8	Kontakt: roman.klinger@ims.uni-stuttgart.deInstitution: Universität Stuttgart, Institut für Maschinelle	6	Kontakt: roman.klinger@ims.uni-stuttgart.deInstitution: Universität Stuttgart, Institut für Maschinelle Sprachverarbeitung GND: 173873820ORCID: 0000-0002-2014-6619
9	Sprachverarbeitung GND: 173873820ORCID: 0000-0002-2014-6619	7
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12		10	DOI: 10.17175/2019_008
13	DOI: 10.17175/2019_008_v2	11	Nachweis im OPAC der Herzog August Bibliothek: 167855300X
14	Nachweis im OPAC der Herzog August Bibliothek: 176443949X
15	Erstveröffentlichung: 16.12.2019	12	Erstveröffentlichung: 16.12.2019
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17	Version 2.0: 23.07.2021	14	Lizenz: Sofern nicht anders angegeben
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		17	Medienlizenzen: Medienrechte liegen bei den Autoren
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		19	Letzte Überprüfung aller Verweise: 27.11.2019
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		21	GND-Verschlagwortung: Gefühl \| Hermeneutik \| Literaturwissenschaft \| Netzwerkanalyse (Soziologie) \| Textanalyse \|
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		23	Empfohlene Zitierweise: 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: 10.17175/2019_008
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		29	Abstract
		30	Emotions are a crucial part of compelling narratives: literature tells us about
		31	people with goals, desires, passions, and intentions. In the past, the
		32	affective dimension of literature was mainly studied in the context of literary
		33	hermeneutics. However, with the emergence of the research field known as
		34	Digital Humanities (DH), some studies of emotions in a literary context have
		35	taken a computational turn. Given the fact that DH is still being formed as a
		36	field, this direction of research can be rendered relatively new. In this
		37	survey, we offer an overview of the existing body of research on sentiment and
		38	emotion analysis as applied to literature. The research under review deals with
		39	a variety of topics including tracking dramatic changes of a plot development,
		40	network analysis of a literary text, and understanding the emotionality of
		41	texts, among other topics.
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		46	Emotionen sind ein wichtiger Bestandteil überzeugender Erzählungen,
		47	Literatur beschreibt schließlich Menschen und ihre Ziele, Wünsche,
		48	Leidenschaften und Absichten. In der Vergangenheit wurde diese affektive
		49	Dimension hauptsächlich im Rahmen der literarischen Hermeneutik
		50	untersucht. Mit dem Aufkommen des Forschungsfeldes Digital Humanities
		51	(DH) wurde jedoch in einigen Studien bezüglich des Aspekts der Emotionen
		52	im literarischen Kontext eine Wende hin zu komputationellen Methoden
		53	vorgenommen. Diese Forschungsrichtung ist aktuell durch die Prozesse in
		54	den DH in einer Neugestaltung. In diesem Artikel berichten wir über den aktuellen
		55	Forschungsstand zur
		56	Sentiment- und Emotionsanalyse zur Analyse von Literatur. Wir behandeln
		57	eine Vielzahl von Themen, wie zum Beispiel die Veränderungen der
		58	emotionalen Konnotation im Verlauf eines Texts, der Netzwerkanalyse
		59	eines literarischen Textes und dem Verständnis der Emotionalität von Texten.
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		61
		62
		63	Zu diesem Artikel ist eine überarbeitete Version erschienen: Version 2
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		65
		66	1 Introduction and Motivation
		67	1.1 Emotions and Arts
		68	2 Affect and Emotion
		69	2.1 Ekman’s Theory of Basic Emotions
		70	2.2 Plutchik’s Wheel of Emotions
		71	2.3 Russel’s Circumplex Model
		72	3 Emotion Analysis in Non-computational Literary Studies
		73	4 Emotion and Sentiment Analysis in Computational Literary Studies
		74	4.1 Emotion Classification
		75	4.1.1 Classification based on emotions
		76	4.1.2 Classification of happy ending vs. non-happy endings
		77	4.2 Genre and Story-type Classification
		78	4.2.1 Story-type clustering
		79	4.2.2 Genre classification
		80	4.3 Temporal Change of Sentiment
		81	4.3.1 Topography of emotions
		82	4.3.2 Tracking sentiment
		83	4.3.3 Sentiment recognition in historical texts
		84	4.4 Character Network Analysis and Relationship Extraction
		85	4.4.1 Sentiment dynamics between characters
		86	4.4.2 Character analysis and character relationships
		87	4.5 Other Types of Emotion Analysis
		88	4.5.1 Emotion flow analysis and visualization
		89	4.5.2 Miscellaneous
		90	5 Discussion and Conclusion
		91	Acknowledgements
		92	Bibliographic References
		93	List of Figures with Captions
		94
		95
		96	1 Introduction and Motivation
		97
		98	This article deals with emotion and sentiment analysis in computational literary studies.
		99	Following Liu[1], we define sentiment as a
		100	positive or negative feeling
		101	underlying the opinion. The term opinion in this sense is
		102	close to attitude in psychology and both sentiment analysis
		103	and opinion mining are often used interchangeably. Sentiment analysis is an area of
		104	computational linguistics that analyzes people’s sentiments and opinions regarding
		105	different objects or topics. Though sentiment analysis is primarily text-oriented,
		106	there are multimodal approaches as well.[2]
		107	Defining the concept of emotion is a challenging task. As
		108	Scherer puts it, defining emotion is a notorious problem.[3] Indeed, different methodological and conceptual
		109	approaches to dealing with emotions lead to different definitions. However, the
		110	majority of emotion theorists agree that emotions involve a set of expressive,
		111	behavioral, physiological, and phenomenological features.[4] In this view, an emotion can be defined as an
		112	integrated feeling state involving physiological changes, motor-preparedness,
		113	cognitions about action, and inner experiences that emerges from an appraisal of the
		114	self or situation.[5]
		115	Similar to sentiment, emotions can be analyzed computationally. However, the goal
		116	of
		117	emotion analysis is to recognize the emotion, rather than sentiment, which makes it
		118	a
		119	more difficult task as differences between emotions are subtler than those between
		120	positive and negative.
		121
		122	Although sentiment and emotion analysis are different tasks, our review of the
		123	literature shows that the use of either term is not always consistent. There are
		124	cases where researchers analyze only positive and negative aspects of a text but
		125	refer to their analysis as emotion analysis. Likewise, there are cases where
		126	researchers look into a set of subjective feelings including emotions but call it
		127	sentiment analysis. Hence, to avoid confusion, in this survey, we use the terms emotion analysis and sentiment analysis
		128	interchangeably. In most cases, we follow the terminology used by the authors of the
		129	papers we discuss (i.e., if they call emotions sentiments, we do the same).
		130
		131	Finally, we talk about sentiment and emotion analysis in the context of computational
		132	literary studies. Da defines computational literary studies as the statistical
		133	representation of patterns discovered in text mining fitted to currently existing
		134	knowledge about literature, literary history, and textual production.[6] Computational literary studies are
		135	synonymous to distant reading[7] and digital
		136	literary studies,[8]
		137	each of which refers to the practice of running a textual analysis on a computer to
		138	yield quantitative results. In this survey, we use all of these terms interchangeably
		139	and when we refer to digital humanities as a field, we refer to those groups of
		140	researchers whose primary objects of study are texts.
		141
		142
		143	1.1 Emotions and Arts
		144
		145	Much of our daily experiences influence and are influenced by the emotions we
		146	experience.[9] This experience is
		147	not limited to real events. People can feel emotions because they are reading a novel
		148	or watching a play or a movie.[10] There is a growing
		149	body of literature that pinpoints the importance of emotions for literary comprehension,
		150	[11] as well as research
		151	that recognizes the deliberate choices people make with regard to their emotional
		152	states when seeking narrative enjoyment such as a book or a film[12]
		153	The link between emotions and arts in general is a matter of debate that dates back
		154	to the Ancient period, particularly to Plato, who viewed passions and desires as the
		155	lowest kind of knowledge and treated poets as undesirable members in his ideal
		156	society.[13] In contrast, Aristotle’s
		157	view on emotive components of poetry expressed in his Poetics[14] differed from Plato’s in that
		158	emotions do have great importance, particularly in the moral life of a person.[15] In the late nineteenth
		159	century the emotion theory of arts stepped into the spotlight of philosophers. One
		160	of
		161	the first accounts on the topic is given by Leo Tolstoy in 1898 in his essay What is Art?.[16] Tolstoy argues that art
		162	can express emotions experienced in fictitious context and the degree to which the
		163	audience is convinced of them defines the success of the artistic work.[17]
		164	New methods of quantitative research emerged in humanities scholarship bringing forth
		165	the so-called digital revolution[18] and the transformation of the
		166	field into what we know as digital humanities.[19] The adoption of computational
		167	methods of text analysis and data mining from the fields of then fast-growing areas
		168	of computational linguistics and artificial intelligence provided humanities scholars
		169	with new tools of text analytics and data-driven approaches to theory
		170	formulation.[20]
		171	To the best of our knowledge, the first work[21] on a computer-assisted modeling of emotions in
		172	literature appeared in 1982. Challenged by the question of why some texts are more
		173	interesting than others, Anderson and McMaster concluded that the emotional tone of
		174	a
		175	story can be responsible for the reader’s interest. The results of their study
		176	suggest that a large-scale analysis of the emotional tone of a collection of texts
		177	is
		178	possible with the help of a computer program. There are two implications of this
		179	finding. First, they suggested that by identifying emotional tones of text passages
		180	one can model affective patterns of a given text or a collection of texts, which in
		181	turn can be used to challenge or test existing literary theories. Second, their
		182	approach to affect modeling demonstrates that the stylistic properties of texts can
		183	be defined on the basis of their emotional interest and not only their linguistic
		184	characteristics. With regard to these implications, this work is an important early
		185	piece as it laid out a roadmap for some of the basic applications of sentiment and
		186	emotion analysis of texts, namely sentiment and emotion pattern recognition from text
		187	and computational text characterization based on sentiment and emotion.
		188
		189	With the development of research methods used by digital humanities researchers, the
		190	number of approaches and goals of emotion and sentiment analysis in literature has
		191	grown. The goal of this survey is to provide an overview of these recent methods of
		192	emotion and sentiment analysis as applied to a text. The survey is directed at
		193	researchers looking for an introduction to the existing research in the field of
		194	sentiment and emotion analysis of a (primarily, literary) text. The survey does not
		195	cover applications of emotion and sentiment analysis in the areas of digital
		196	humanities that are not focused on text. Neither does it provide an in-depth overview
		197	of all possible applications of emotion analysis in the computational context outside
		198	of the DH line of research.
		199
		200
		201
		202
		203	2 Affect and Emotion
		204
		205	The history of emotion research has a long and rich tradition that followed Darwin’s
		206	1872 publication of The Expression of the Emotions in Man and Animals[22]. The subject of emotion theories is vast
		207	and diverse. We refer the reader to Maria Gendron’s paper[23] for a brief history of ideas about emotion
		208	in psychology. Here, we will focus on three views on emotion that are popular in
		209	computational analysis of emotions: Ekman’s theory of basic
		210	emotions, Plutchik’s wheel of emotion, and Russel’s
		211	circumplex model.
		212
		213
		214	2.1 Ekman’s Theory of Basic Emotions
		215
		216	The basic emotion theory was first articulated by Silvan Tomkins[24] in the early 1960s. Tomkins postulated that each instance
		217	of a certain emotion is biologically similar to other instances of the same emotion
		218	or shares a common trigger. One of Tomkins’ mentees, Paul Ekman, put in question the
		219	existing emotion theories that proclaimed that facial expressions of emotion are
		220	socially learned and therefore vary from culture to culture. Ekman, Sorenson and
		221	Friesen challenged this view[25]
		222	in a field study with the outcome that facial displays of fundamental emotions are
		223	not learned but innate. However, there are culture-specific prescriptions about how
		224	and in which situations emotions are displayed.
		225
		226	Based on the observation of facial behavior in early development or social
		227	interaction, Ekman’s theory also postulates that emotions should be considered discrete categories[26]
		228	rather than continuous. Though this
		229	view allows for conceiving of emotions as having different intensities, it does not
		230	allow emotions to blend and leaves no room for more complex affective states in which
		231	individuals report the co-occurrence of like-valenced discrete
		232	emotions.[27] This and other theory
		233	postulates were widely criticized and disputed in literature.[28]
		234
		235
		236	2.2 Plutchik’s Wheel of Emotions
		237
		238	Another influential model of emotions was proposed by Robert Plutchik in the early
		239	1980s.[29] The important difference
		240	between Plutchik’s theory and Ekman’s theory is that apart from a small set of basic
		241	emotions, all other emotions are mixed and derived from the various combinations of
		242	basic ones. He further categorized these other emotions into the primary dyads (very likely to co-occur), secondary
		243	dyads (less likely to co-occur) and tertiary dyads
		244	(seldom co-occur).
		245
		246	In order to represent the organization and properties of emotions as defined by his
		247	theory, Plutchik proposed a structural model of emotions known nowadays as Plutchik’s wheel of emotions. The wheel Figure 1 is constructed in the fashion of a color wheel, with
		248	similar emotions placed closer together and opposite emotions 180 degrees apart. The
		249	intensity of an emotion in the wheel depends on how far from the center a part of
		250	a
		251	petal is, i.e., emotions become less distinguishable the further they are from the
		252	center of the wheel. Essentially, the wheel is constructed from eight basic bipolar
		253	emotions: joy versus sorrow, anger versus fear, trust versus disgust, and surprise versus anticipation. The blank spaces
		254	between the leaves are so-called primary dyads – emotions that
		255	are mixtures of two of the primary emotions.
		256
		257	The wheel model of emotions proposed by Plutchik had a great impact on the field of affective computing
		258	being primarily used as a basis for emotion categorization in emotion recognition
		259	from text.[30] However, some postulates of the theory are criticized,
		260	for example, there is no empirical support for the wheel structure.[31] Another criticism is that
		261	Plutchik’s model of emotion does not explain the mechanisms by which love, hate, relief, pride, and other everyday emotions emerge
		262	from the basic emotions, nor does it provide reliable
		263	measurements of these emotions.[32]
		264
		265
		266	Fig. 1: Plutchik’s wheel of emotions. [Plutchik 2011.
		267	PD]
		268
		269
		270
		271
		272	2.3 Russel’s Circumplex Model
		273
		274	Attempts to overcome the shortcomings of basic emotions theory and its unfitness for
		275	clinical studies led researchers to suggest various dimensional models, the most
		276	prominent of which is the circumplex model of affect proposed by James Russel.[33] The word circumplex
		277	in the name of the model refers to the fact that emotional episodes do not cluster
		278	at
		279	the axes but rather at the periphery of a circle Figure 2. At the core of the
		280	circumplex model is the notion of two dimensions plotted on a circle along horizontal
		281	and vertical axes. These dimensions are valence (how pleasant
		282	or unpleasant one feels) and arousal (the degree of calmness
		283	or excitement). The number of dimensions is not strictly fixed and there are
		284	adaptations of the model that incorporate more dimensions. One example of this is
		285	the
		286	Valence-Arousal-Dominance model that adds an additional
		287	dimension of dominance, the degree of control one feels over the situation that
		288	causes an emotion.[34]
		289	By moving from discrete categories to a dimensional representation, the researchers
		290	are able to account for subjective experiences that do not fit nicely into the
		291	isolated non-overlapping categories. Accordingly, each affective experience can be
		292	depicted as a point in a circumplex that is described by only
		293	two parameters – valence and arousal –
		294	without need for labeling or reference to emotion concepts for which a name might
		295	only exist in particular subcommunities or which are difficult to describe.[35] However, the strengths of the model turned
		296	out to be its weaknesses: for example, it is not clear whether there are basic
		297	dimensions in the model[36] nor is it
		298	clear what should be done with qualitatively different events of fear, anger, embarrassment and
		299	disgust that fall in identical places in the circumplex
		300	structure.[37] Despite these
		301	shortcomings, the circumplex model of affect is widely used in psychologic and
		302	psycholinguistic studies. In computational linguistics, the circumplex model is
		303	applied when the interest is in continuous measurements of valence and arousal rather than in the specific
		304	discrete emotional categories.
		305
		306
		307
		308	Fig. 2: Circumplex model of affect: Horizontal axis represents the valence dimension,
		309	the vertical axis represents the arousal dimension. Drawn after Posner et al. 2005. [Kim / Klinger 2019]
		310
		311
		312
		313
		314	3 Emotion Analysis in Non-computational Literary Studies
		315
		316	Until the end of the twentieth century, literary and art theories often disregarded
		317	the importance of the aesthetic and affective dimension of literature, which in part
		318	stemmed from the rejection of old-fashioned literary history that had explained the
		319	meaning of art works by the biography of the author.[38] However, the affective turn taken by a wide range of
		320	disciplines in the past two decades – from political and sociological sciences to
		321	neurosciences or media studies – has refueled the interest of literary critics in
		322	human affects and sentiments.
		323
		324	We said in Section 1 that there seems to be a consensus among literary critics that
		325	literary art and emotions go hand in hand. However, one might be challenged to define
		326	the specific way in which emotions come into play in the text. The exploration of
		327	this problem is presented by van Meel.[39]
		328	Underpinning the centrality of human destiny, hopes, and feelings in the themes of
		329	many artworks – from painting to literature – van Meel explores how emotions are
		330	involved in the production of arts. Pointing out big differences between the two
		331	media in their attempts to depict human emotions (painting conveys nonverbal behavior
		332	directly, but lacks temporal dimensions that novels have and use to describe
		333	emotions), van Meel provides an analysis of the nonverbal descriptions used by the
		334	writers to convey their characters’ emotional behavior. Description of visual
		335	characteristics, van Meel speculates, responds to a fundamental need of a reader to
		336	build an image of a person and their behavior. Moreover, nonverbal descriptions add
		337	important information that can in some cases play a crucial hermeneutical role, such
		338	as in Kafka’s Der Prozess, where the fatal decisions for K. are made clear by gestures rather than
		339	words. His verdict is not announced, but is implied by the judge who refuses a
		340	handshake. The same applies to his death sentence that is conveyed to him by his
		341	executioners playing with a butcher’s knife above his head.
		342
		343	A hermeneutic approach through the lense of emotions is presented by Kuivalainen[40] and provides a detailed analysis of
		344	linguistic features that contribute to the characters’ emotional involvement in
		345	Mansfield’s prose. The study shows how, through the extensive use of adjectives,
		346	adverbs, deictic markers, and orthography, Mansfield steers the reader towards the
		347	protagonist’s climax. Subtly shifting between psycho-narration and free indirect
		348	discourse, Mansfield is making use of evaluative and emotive descriptors in
		349	psycho-narrative sections, often marking the internal discourse with dashes,
		350	exclamation marks, intensifiers, and repetition that thus trigger an emotional
		351	climax. Various deictic features introduced in the text are used to pinpoint the
		352	source of emotions, which helps in creating a picture of characters’ emotional world.
		353	Verbs (especially in the present tense), adjectives, and adverbs serve the same goal
		354	in Mansfield’s prose of describing the characters’ emotional world. Going back and
		355	forth from psycho-narration to free indirect discourse provides Mansfield with a tool
		356	to point out the significant moments in the protagonists’ lives and establish a
		357	separation between characters and narration.
		358
		359	Both van Meel’s and Kuivalainen’s works, separated from each other by more than a
		360	decade, underpin the importance of emotions in the interpretation of characters’
		361	traits, hopes, and tragedy. Other authors find these connections as well. For
		362	example, Barton[41] proposes instructional
		363	approaches to teach school-level readers to interpret character’s emotions and use
		364	this information for story interpretation. Van Horn[42] shows that understanding characters emotionally or trying to help
		365	them with their problems made reading and writing more meaningful for middle school
		366	students.
		367
		368	Emotions in text are often conveyed with emotion-bearing words.[43] At the same time their role in the creation
		369	and depiction of emotion should not be overestimated. That is, saying that someone
		370	looked angry or fearful or sad, as well as directly expressing characters’ emotions,
		371	are not the only ways authors build believable fictional spaces filled with
		372	characters, action, and emotions. In fact, many novelists strive to express emotions
		373	indirectly by way of figures of speech or catachresis,[44] first of all because emotional language can be
		374	ambiguous and vague, and, second, to avoid any allusions to Victorian emotionalism
		375	and pathos.
		376
		377	How can an author convey emotions indirectly? A book chapter by Hillis Miller in Exploring Text and Emotions[45] seeks the answer to exactly this
		378	question. Using Conrad’s Nostromo opening scenes as material, Hillis Miller shows how Conrad’s descriptions of
		379	an imaginary space generate emotions in readers without direct communication of
		380	emotions. Conrad’s Nostromo opening chapter is an objective description of Sulaco, an imaginary land. The
		381	description is mainly topographical and includes occasional architectural metaphors,
		382	but it combines wide expanse with hermetically sealed enclosure, which generates
		383	depthless emotional detachment[46]. Through the use of present tense, Conrad makes the readers suggest
		384	that the whole scene is timeless and does not change. The topographical descriptions
		385	are given in a pure materialist way: there is nothing behind clouds, mountains,
		386	rocks, and sea that would matter to humankind, not a single feature of the landscape
		387	is personified, and not a single topographical shape is symbolic. Knowingly or
		388	unknowingly, Miller argues, by telling the readers what they should see – with no
		389	deviations from truth – Conrad employs a trope that perfectly matches Kant’s concept of the sublime. Kant’s view of poetry was that true poets tell the truth without
		390	interpretation; they do not deviate from what their eyes see. Conrad, or to be more
		391	specific, his narrator in Nostromo, is an example of sublime seeing with a latent presence of strong emotions.
		392	On the one hand, Conrad’s descriptions are cool and detached. This coolness is caused
		393	by the indifference of the elements in the scene. On the other hand, by dehumanizing
		394	sea and sky, Conrad generates awe, fear, and a dark foreboding about the kinds of
		395	life stories that are likely to be enacted against such a backdrop[47].
		396
		397	Hillis Miller’s analysis resonates with some premises from emotion theory that we
		398	have discussed previously, namely, Plutchik’s belief that emotions should be studied
		399	not by a certain way of expression but by the overall behavior of a person.
		400	Considering that such a formula cannot be applied to all literary theory studies
		401	about emotions (as not all authors choose to convey emotions indirectly, as well as
		402	not all authors tend to comment on characters’ nonverbal emotional behavior), it
		403	seems that one should search for a balance between low-level linguistic feature
		404	analysis of emotional language and a rigorous high-level hermeneutic inquiry
		405	dissecting the form of the novel and its under-covered philosophical layers.
		406
		407
		408
		409	4 Emotion and Sentiment Analysis in Computational Literary Studies
		410
		411	With this section, we proceed to an overview of the existing body of research on
		412	computational analysis of emotion and sentiment in computational literary studies.
		413	An
		414	overview of the papers including their properties is shown in
		415	Table 1. The table, as
		416	well as this section, is divided into several subsections, each of which corresponds
		417	to a specific application of emotion and sentiment analysis to literature.
		418	Section 4.1 reviews the papers that deal with the classification of literary texts in terms
		419	of emotions they convey; Section 4.2 examines the papers that address text
		420	classification by genre or other story-types based on sentiment and emotion features;
		421	Section 4.3 is dedicated to research in modeling sentiments and emotions in texts
		422	from previous centuries, as well as research dealing with applications of sentiment
		423	analysis to texts written in the past; Section 4.4 provides an overview of sentiment
		424	analysis applications to character analysis and character network construction, and
		425	Section 4.5 is dedicated to more general applications of sentiment and emotion
		426	analysis to literature.
		427
		428
		429	4.1 Emotion Classification
		430
		431	A straightforward approach to sentiment and emotion analysis is phrasing them as a
		432	text classification[48]. A fundamental
		433	question of such a classification is how to find the best features and algorithms
		434	to
		435	classify the data (sentences, paragraphs, entire documents) into predefined classes.
		436	When applied to literature, such a classification may be of use for grouping
		437	different literary texts in digital collections based on the emotional properties
		438	of
		439	the stories. For example, books or poems can be grouped based on the emotions they
		440	convey or based on whether or not they have happy endings or not.
		441
		442
		443	4.1.1 Classification based on emotions
		444
		445	Barros et al.[49] aim at answering two
		446	research questions: 1) is the classification of Quevedo’s works proposed by the
		447	literary scholars consistent with the sentiment reflected by the corresponding
		448	poems?; and 2) which learning algorithms are the best for the classification? To that
		449	end, they perform a set of experiments on the classification of 185 Francisco de
		450	Quevedo’s poems that are divided by literary scholars into four categories and that
		451	Barros et al. map to emotions of joy, anger, fear, and sadness.
		452	Using the terms joy, anger, fear, and sadness as points of
		453	reference, Barros et al. construct a list of emotion words by looking up the synonyms
		454	of English emotion words and adjectives associated with these four emotions and
		455	translating them into Spanish. Each poem is converted into a vector where each item
		456	is a normalized count of words relating to a certain emotion. The experiments with
		457	different algorithms show the superiority of decision trees achieving accuracy of
		458	almost 60%. However, this result is biased by an unbalanced distribution of classes.
		459	To avoid the bias, Barros et al. apply a resampling strategy that leads to a more
		460	balanced distribution and repeat the classification experiments. After resampling,
		461	the accuracy of decision trees in a 10-fold cross validation achieves 75,13%, thus
		462	demonstrating an improvement over the previous classification performance. Based on
		463	these results the authors conclude that a meaningful classification of the literary
		464	pieces based only on the emotion information is possible.
		465
		466	Reed[50] offers a proof-of-concept for performing sentiment analysis on a corpus of
		467	twentieth-century American poetry. Specifically, Reed analyzes the expression of
		468	emotions in the poetry of the Black Arts Movement of the 1960s
		469	and 1970s. The paper describes the project Measured Unrest in the Poetry of the Black
		470	Arts Movement whose goal is to understand 1) how the feelings associated with
		471	injustice are coded in terms of race and gender, and 2) what sentiment analysis can
		472	show us about the relations between affect and gender in poetry. Reed notes that
		473	surface affective value of the words does not always align with their more nuanced
		474	affective meaning shaped by poetic, social, and political contexts.
		475
		476	Yu[51] explores what linguistic patterns
		477	characterize the genre of sentimentalism in early American novels. To that end, they
		478	construct a collection of five novels from the mid-nineteenth century and annotate
		479	the emotionality of each of the chapters as high or low. The respective chapters are then classified using
		480	support-vector machines and naïve Bayes classifiers as highly emotional or the
		481	opposite. The results of the evaluation suggest that arbitrary feature reduction
		482	steps such as stemming and stopword removal should be taken very carefully, as they
		483	may affect the prediction. For example, Yu shows that no stemming leads to better
		484	classification results. A possible explanation is that stemming conflates and
		485	neutralizes a large number of discriminative features. The author provides an example
		486	of such a conflation with the words wilderness and wild. While the latter can appear anywhere in the text, the
		487	former one is primarily encountered in the chapters filled with emotions.
		488
		489
		490
		491	4.1.2 Classification of happy ending vs. non-happy endings
		492
		493	Zehe et al.[52] argue that automatically
		494	recognizing a happy ending as a major plot element could help to better understand
		495	a
		496	plot structure as a whole. To show that this is possible, they classify 212 German
		497	novels written between 1750 and 1920 as having happy or non-happy endings. A novel
		498	is
		499	considered to have a happy ending if the situation of the main characters in the
		500	novel improves towards the end or is constantly favorable. The novels were manually
		501	annotated with this information by domain experts. For feature extraction, the
		502	authors first split each novel into n segments of the same
		503	length. They then calculate sentiment values for each of the segments by counting
		504	the
		505	occurrences of words that appear in the respective segment and that are found in the
		506	German version of the NRC Word-Emotion Association Lexicon[53] and divide this number by the
		507	length of the dictionary. Finally, they calculate the sentiment score for the
		508	sections by taking the average of all sentiment scores in the segments that are part
		509	of the section. These steps are then followed by classification with a support-vector
		510	machine and the F1 score of 0.73, which the authors consider a good starting point
		511	for future work.
		512
		513
		514
		515
		516	4.2 Genre and Story-type Classification
		517
		518	The papers we have discussed so far focus on understanding the emotion associated
		519	with units of texts. This extracted information can further be used for downstream
		520	tasks and also for downstream evaluations. We discuss the following downstream
		521	classification cases here. The papers in this category use sentiment and emotion
		522	features for a higher-level classification, namely story-type clustering and literary
		523	genre classification. The assumption behind these works is that different types of
		524	literary text may show different composition and distribution of emotion vocabulary
		525	and thus can be classified based on this information. The hypothesis that different
		526	literary genres convey different emotions stems from common knowledge: we know that
		527	horror stories instill fear and that mysteries evoke anticipation and anger while romances
		528	are filled with joy and love. However
		529	as we will see in this section, the task of automatic classification of these genres
		530	is not always that straightforward and reliable.
		531
		532
		533	4.2.1 Story-type clustering
		534
		535	Similarly to Zehe et al., Reagan et al.[54] are interested in automatically understanding a plot structure as a
		536	whole, not limited to a book ending. The inspiration for their work comes from Kurt
		537	Vonnegut’s lecture on emotional arcs of stories.[55]
		538	Reagan et al. test the idea that the plot
		539	of each story can be plotted as an emotional arc, i.e. a time
		540	series graph, where the x-axis represents a time point in a
		541	story, and the y-axis represents the events happening to the
		542	main characters that can be favorable (peaks on a graph) or unfavorable (troughs on
		543	a
		544	graph). As Vonnegut puts it, the stories can be grouped by these arcs and the number of such groupings is limited. To test this idea, Reagan
		545	et al. collect the 1,327 most popular books from the Project Gutenberg.[56] Each book is then split into segments for which
		546	sentiment scores (happy vs. sad) are
		547	calculated and compared. The results of the analysis show support for six emotional
		548	patterns that are shared between subgroupings of the corpus:
		549
		550
		551	Rise: the arc starts at a low point and steadily increases towards the end;
		552	Fall: the arc starts at a high point and steadily decreases towards the end;
		553	Fall-rise: the arc drops in the middle of the story but increases towards the
		554	end;
		555
		556	Rise-fall: the arc hits the high point in the middle of the story and decreases
		557	towards the end;
		558
		559	Rise-fall-rise: the arc fluctuates between high and low points but ends with an
		560	increase;
		561
		562	Fall-rise-fall: the arc fluctuates between high and low points but ends with a
		563	decrease.
		564
		565
		566	Additionally, Reagan et al. find that Icarus, Oedipus, and Man in the hole arcs are
		567	the three most popular emotional arcs among readers, based on download counts.
		568
		569
		570
		571	4.2.2 Genre classification
		572
		573	There are other studies[57] that are similar in spirit to the work done by
		574	Reagan. Samothrakis and Fasli examine the hypothesis that different genres clearly
		575	have different emotion patterns to reliably classify them with machine learning. To
		576	that end, they collect works of the genres mystery, humor, fantasy, horror, science fiction and western from the Project Gutenberg.
		577
		578	Using WordNet-Affect[58] to
		579	detect emotion words as categorized by Ekman’s fundamental emotion classes, they
		580	calculate an emotion score for each sentence in the text. Each work is then
		581	transformed into six vectors, one for each basic emotion. A random forest classifier
		582	achieves a classification accuracy of 0.52. This is significantly higher than a
		583	random baseline, which allows the authors to conclude that such a classification is
		584	feasible.
		585
		586	A study by Kim et al.[59] originates from
		587	the same premise as the work by Samothrakis and Fasli but puts emphasis on finding
		588	genre-specific correlations of emotion developments. Extending the set of tracked
		589	emotions to Plutchik’s classification, Kim et al. collect 2,000 books from the
		590	Project Gutenberg that belong to five genres found in the Brown corpus[60], namely adventure, science fiction, mystery, humor and romance.
		591	The authors extend the set of classification algorithms beyond random forests using
		592	a
		593	multi-layer perceptron and convolutional
		594	neural networks, which achieves the best performance (0.59 F1-score). To
		595	understand how uniform the emotion patterns in different genres are, the authors
		596	introduce the notion of prototypicality, which is computed as
		597	average of all emotion scores. Using this as a point of reference for each genre Kim
		598	et al. use Spearman correlation to calculate the uniformity of emotions per genre.
		599	The results of this analysis suggest that fear and anger are the most salient plot devices in fiction, while joy is only of mediocre stability, which is in line with
		600	findings of Samothrakis and Fasli.
		601
		602	The study by Henny-Krahmer[61] pursues
		603	two goals: 1), to test whether different subgenres of Spanish American literature
		604	differ in degree and kind of emotionality, and 2), whether emotions in the novels
		605	are
		606	expressed in direct speech of characters or in narrated text. To that end, they
		607	conduct a subgenre classification experiment on a corpus of Spanish American novels
		608	using sentiment values as features. To answer the first question, each novel is split
		609	into five segments and for each sentence in the segment the emotion score (polarity
		610	values + Plutchik’s basic emotions) is calculated using SentiWordNet[62] and NRC[63] dictionaries. The classifier achieves an average F1
		611	of 0.52, which is higher than the most-frequent class baseline and, hence, provides
		612	a
		613	support for emotion-based features in subgenre classification. The analysis of
		614	feature importance shows that the most salient features come from the sentiment
		615	scores calculated from the characters’ direct speech and that novels with higher
		616	values of positive speech are more likely to be sentimental novels.
		617
		618	There are some limitations to the studies presented in this section. On the one hand,
		619	it is questionable how reliable coarse emotion scoring is that
		620	takes into account only presence or absence of words found in specialized
		621	dictionaries and overlooks negations and modifiers that can either negate an emotion
		622	word or increase/decrease its intensity. On the other hand, a limited view of the
		623	emotional content as a sum of emotion bearing words reserves no room for qualitative
		624	interpretation of the texts – it is not clear how one can distinguish between emotion
		625	words used by the author to express their sentiment, between words used to describe
		626	characters’ feelings, and emotion words that characters use to address or describe
		627	other characters in a story.
		628
		629
		630
		631
		632	4.3 Temporal Change of Sentiment
		633
		634	The papers that we have reviewed so far approach the problem of sentiment and emotion
		635	analysis as a classification task. However, applications of sentiment analysis are
		636	not only limited to classification. In other fields, for example computational social
		637	sciences, sentiment analysis can be used for analyzing political preferences of the
		638	electorate or for mining opinions about different products or topics. Similarly,
		639	several digital humanities studies incorporate sentiment analysis methods in a task
		640	of mining sentiments and emotions of people who lived in the past. The goal of these
		641	studies is not only to recognize sentiments, but also to understand how they were
		642	formed.
		643
		644
		645	4.3.1 Topography of emotions
		646
		647	Heuser et al.[64] start with a premise
		648	that emotions occur at a specific moment in time and space, thus making it possible
		649	to link emotions to specific geographical locations. Consequently, having such
		650	information at hand, one can understand which emotions are hidden behind certain
		651	landmarks. As a proof-of-concept, Heuser et al. build an interactive map,
		652	Mapping emotions in Victorian London[65], where each location is tagged with emotion
		653	labels. To construct a corpus for their analysis, Heuser et al. collect a large
		654	corpus of English books from the eighteenth and nineteenth century and extract 383
		655	geographical locations of London that have at least ten mentions each. The resulting
		656	corpus includes 15,000 passages, each of which has a toponym in the middle and 100
		657	words directly preceding and following the location mention. The data is then given
		658	to annotators who are asked to define whether each of the passages expressed happiness or fear, or neutrality. The same data is also analyzed by a custom sentiment analysis
		659	program that would assign each passage one of these emotion categories.
		660
		661	Some striking observations are made with regard to the data analysis. First, there
		662	is
		663	a clear discrepancy between fiction and reality – while toponyms from the West End
		664	with Westminster and the City are over-represented in the books, the same does not
		665	hold true for the East End with Tower Hamlets, Southwark, and Hackney. Hence, there
		666	is less information about emotions pertaining to these particular London locations.
		667	Another striking detail is that the resulting map is dominated by the neutral
		668	emotion. Heuser et al. argue that this has nothing to do with the absence of emotions
		669	but rather stems from the fact that emotions tend to be silenced in public domain,
		670	which influenced the annotators decision.
		671
		672	The space and time context are also used by Bruggman and Fabrikant[66] who model sentiments of Swiss
		673	historians towards places in Switzerland in different historical periods. As the
		674	authors note, it is unlikely that a historian will directly express attitudes towards
		675	certain toponyms, but it is very likely that words they use to describe those can
		676	bear some negative connotation (e.g. cholera, death). Correspondingly, such places
		677	should be identified as bearing negative sentiment by a sentiment analysis tool.
		678	Additionally, they study the changes of sentiment towards a particular place over
		679	time. Using the General Inquirer (GI) lexicon[67] to identify
		680	positive and negative terms in the document, they assign each document a sentiment
		681	score by summing up the weights of negative and positive words and normalizing them
		682	by the document length. The authors conclude that the results of their analysis look
		683	promising, especially regarding negatively scored articles. However, the authors find
		684	difficulties in interpreting positively ranked documents, which may be due to the
		685	fact that negative information is more salient.
		686
		687
		688
		689	4.3.2 Tracking sentiment
		690
		691	Other papers in this category link sentiment and emotion to certain groups, rather
		692	than geographical locations. The goal of these studies is to understand how sentiment
		693	within and towards these groups was formed.
		694
		695	Taboada et al.[68]
		696	aim at tracking the literary reputation of six authors writing in the first half of
		697	the twentieth century. The research questions raised in the project are how the
		698	reputation is made or lost, and how to find correlation between what is written about
		699	the author and their work to the author’s reputation and subsequent canonicity. To
		700	that end, the project’s goal is to examine critical reviews of six authors’ writing
		701	and to map information contained in texts critical to the author’s reputation. The
		702	material they work with includes not only reviews, but also press notes, press
		703	articles, and letters to editors (including from the authors themselves). For the
		704	pilot project with Galsworthy and Lawrence they collected and scanned 330 documents
		705	(480,000 words). The documents are tagged for the parts of speech and relevant words
		706	(positive and negative) are extracted using custom-made sentiment dictionaries. The
		707	sentiment orientation of rhetorically important parts of the texts is then measured.
		708
		709
		710	Chen et al.[69] aim to understand personal
		711	narratives of Korean comfort women who had been forced into
		712	sexual slavery by Japanese military during World War II. Adapting the WordNet-Affect lexicon,[70] Chen et
		713	al. build their own emotion dictionary to spot emotional keywords in women’s stories
		714	and map the sentences to emotion categories. By adding variables of time and space,
		715	Chen et al. provide a unified framework of collective remembering of this historical
		716	event as witnessed by the victims.
		717
		718	Finally, an interesting project to follow is the Oceanic Exchanges[71] project that started in late 2017. One goal of the project is
		719	to trace information exchange in nineteenth-century newspapers and journals using
		720	sentiment as one of the variables under analysis.
		721
		722
		723
		724	4.3.3 Sentiment recognition in historical texts
		725
		726	Other papers put emphasis not so much on the sentiments expressed by writers but
		727	instead focus on the particularities of historical language.
		728
		729	Marchetti et al.[72] and Sprugnoli et
		730	al.
		731	[73] present the integration of
		732	sentiment analysis in the ALCIDE (Analysis of Language and Content In a Digital Environment)
		733	project[74]. The sentiment analysis module is
		734	based on WordNet-Affect, SentiWordNet[75] and MultiWordNet.[76] Each
		735	document is assigned a polarity score by summing up the words with prior polarity
		736	and
		737	dividing by the number of words in the document. A positive global score leads to
		738	a
		739	positive document polarity and a negative global score leads to a negative document
		740	polarity. The overall conclusion of their work is that the assignment of a polarity
		741	in the historical domain is a challenging task largely due to lack of agreement on
		742	polarity of historical sources between human annotators.
		743
		744	Challenged by the problem of applicability of existing emotion lexicons to historical
		745	texts, Buechel et al.[77] propose a new
		746	method of constructing affective lexicons that would adapt well to German texts
		747	written up to three centuries ago. In their study, Buechel et al. use the
		748	representation of affect based on the Valence-Arousal-Dominance
		749	model (an adaptation of Russel’s circumplex model, see Section 2.3).
		750	Presumably, such a representation provides a finer-grained insight into the literary
		751	text,[78] which is more expressive
		752	than discrete categories, as it quantifies the emotion along three different
		753	dimensions. As a basis for the analysis, they collect German texts from the Deutsches Textarchiv[79] written
		754	between 1690 and 1899. The corpus is split into seven slices, each spanning 30 years.
		755	For each slice they compute word similarities and obtain seven distinct emotion
		756	lexicons, each corresponding to specific time period. This allows for, the authors
		757	argue, the tracing of the shift in emotion association of words over time.
		758
		759	Finally, Leemans et al.[80] aim to
		760	trace historical changes in emotion expressions and to develop methods to trace these
		761	changes in a corpus of 29 Dutch language theatre plays written between 1600 and 1800.
		762	Expanding the Dutch version of Linguistic Inquiry and Word Count (LIWC) dictionary[81] with
		763	historical terms, the authors are able to increase the recall of emotion recognition
		764	with a dictionary. In addition, they develop a fine-grained vocabulary mapping body
		765	terms to emotions, and show that a combination of LIWC and their lexicon lead to
		766	improvement in the emotion recognition.
		767
		768
		769
		770
		771	4.4 Character Network Analysis and Relationship Extraction
		772
		773	The papers reviewed above address sentiment analysis of literary texts mainly on a
		774	document level. This abstraction is warranted if the goal is to get an insight into
		775	the distribution of emotions in a corpus of books. However, emotions depicted in
		776	books do not exist in isolation but are associated with characters who are at the
		777	core of any literary narrative.[82] This leads us to ask what sentiment and emotion analysis can tell us
		778	about the characters. How emotional are they? And what role do emotions play in their
		779	interaction?
		780
		781	Character relationships have been analyzed in computational linguistics from a graph
		782	theoretic perspective, particularly using social network analysis.[83] Fewer works,
		783	however, address the problem of modeling character relationships in terms of
		784	sentiment. Below we provide an overview of several papers that propose the
		785	methodology for extracting this information.
		786
		787
		788	4.4.1 Sentiment dynamics between characters
		789
		790	Several studies present automatic methods for analyzing sentiment dynamics between
		791	plays’ characters. The goal of the study by Nalisnick and Baird[84] is to track the emotional trajectories of
		792	interpersonal relationships. The structured format of a dialog allows them to
		793	identify who is speaking to whom, which makes it possible to mine
		794	character-to-character sentiment by summing the valence values of words that appear
		795	in the continuous direct speech and are found in the lexicon[85]
		796	of affective norms. The extension[86] of the previous research from the same authors
		797	introduces the concept of a sentiment network, a dynamic social network of
		798	characters. Changing polarities between characters are modeled as edge weights in
		799	the
		800	network. Motivated by the desire to explain such networks in terms of a general
		801	sociological model, Nalisnick and Baird test whether Shakespeare’s plays obey the
		802	Structural Balance Theory by Marvel et al.[87] that postulates that a friend of a
		803	friend is also your friend. Using the procedure proposed by Marvel et al. on their
		804	Shakespearean sentiment networks, Nalisnick and Baird test whether they can predict
		805	how a play’s characters will split into factions using only information about the
		806	state of the sentiment network after Act II. The results of their analysis are varied
		807	and do not provide adequate support for the Structural Balance Theory as a benchmark
		808	for network analysis in Shakespeare’s plays. One reason for that, as the authors
		809	state, is inadequacy of their shallow sentiment analysis methods that cannot detect
		810	such elements of speech as irony and deceit that play a pivotal role in many literary
		811	works.
		812
		813
		814
		815	4.4.2 Character analysis and character relationships
		816
		817	Elsner[88] aims at answering the
		818	question of how to represent a plot structure for summarization and generation tools.
		819	To that end, Elsner presents a kernel for comparing novelistic
		820	plots at the level of character interactions and their relationships. Using sentiment
		821	as one of the characteristics of a character, Elsner demonstrates that the kernel
		822	approach leads to meaningful plot representation that can be used for a higher-level
		823	processing.
		824
		825	Kim and Klinger[89] aim at understanding
		826	the causes of emotions experienced by literary characters. To that end, they
		827	contribute the REMAN
		828	corpus[90] of literary texts with annotations of emotions,
		829	experiencers, causes and targets of the emotions. The goal of the project is to
		830	enable the automatic extraction of emotions and causes of emotions experienced by
		831	the
		832	characters. The authors suggest that the results of coarse-grained emotion
		833	classification in literary text are not readily interpretable as they do not tell
		834	much about who the experiencer of the emotion is. Indeed, if a text mentions two
		835	characters, one of whom is angry and another one who is scared because of that, text classification models will only
		836	tell us that the text is about anger and fear. Hence, a finer-grained approach towards character relationship
		837	extraction is warranted. Kim and Klinger conduct experiments on the annotated dataset
		838	showing that the fine-grained approach to emotion prediction with long short-term
		839	memory networks outperforms bag-of-words models (an increase
		840	in F1 by 12 pp). At the same time, the results of their experiments suggest that
		841	joint prediction of emotions and experiencers can be more beneficial than studying
		842	these categories separately.
		843
		844	Barth et al.[91] develop the character
		845	relation analysis tool rCAT with the goal of visualization and
		846	analysis of character networks in a literary text. The tool implements a distance
		847	parameter (based on token space) for finding pairs of interacting characters. In
		848	addition to the general context words that characterize each pair of characters, the
		849	tool provides an emotion filter to restrict character relationship analysis to
		850	emotions only.
		851
		852	A tool presented by Jhavar and Mirza[92] provides a similar functionality: given an input of two character
		853	names from the Harry Potter series, the EMoFiel[93] tool identifies the emotion flow between a
		854	given directed pair of story characters. These emotions are identified using
		855	categorical[94] and continuous[95] emotion models.
		856
		857	Egloff et al.[96] present an ongoing
		858	work on the Ontology of Literary Characters (OLC) that allows
		859	us to capture and infer characters’ psychological traits from their linguistic
		860	descriptions. The OLC incorporates the Ontology of Emotion[97] that is based on both Plutchik’s and
		861	Hourglass’s[98] models of emotions.
		862	The ontology encodes 32 emotion concepts. Based on their natural language
		863	description, characters are attributed to a psychological profile along the classes
		864	of Openness to experience, Conscientiousness, Extraversion, Agreeableness, and Neuroticism. The ontology links
		865	each of these profiles to one or more archetypal categories of hero, anti-hero, and villain.
		866	Egloff et al. argue that, by using the semantic connections of the OLC, it is
		867	possible to infer the characters’ psychological profiles and the role they play in
		868	the plot.
		869
		870	Kim and Klinger[99] propose a new task
		871	of emotion relationship classification between fictional characters. They argue that
		872	joining character network analysis with sentiment and emotion analysis may contribute
		873	to a computational understanding of narrative structures, as characters are at the
		874	center of any plot development. Building a corpus of 19 fan fiction short stories
		875	and
		876	annotating it with emotions, Kim and Klinger propose several models to classify
		877	emotion relations of characters. They show that a deep learning architecture with
		878	character position indicators is the best for the task of predicting both directed
		879	and undirected emotion relations in the associated social network graph. As an
		880	extension to this study, Kim and Klinger[100] explore how emotions are expressed between characters in the same
		881	corpus via various non-verbal communication channels.[101] They find
		882	that facial expressions are predominantly associated with joy
		883	while gestures and body postures are more likely to occur with trust.
		884
		885	Finally, a small body of work focuses on mathematical modeling of character
		886	relationships. Rinaldi et al.[102]
		887	contribute a model that describes the love story between the Beauty and the Beast
		888	through ordinary differential equations. Zhuravlev et al.[103] introduce a distance function to model the
		889	relationship between the protagonist and other characters in two masochistic short
		890	novels by Ivan Turgenev and Sacher-Masoch. Borrowing some instruments from the
		891	literary criticism and using ordinary differential equations, Zhuravlev et al. are
		892	able to reproduce the temporal and spatial dynamics of the love plot in the two
		893	novellas more precisely than it had been done in previous research. Jafari et
		894	al.[104] present a dynamic model
		895	describing the development of character relationships based on differential
		896	equations. The proposed model is enriched with complex variables that can represent
		897	complex emotions such as coexisting love and hate.
		898
		899
		900
		901
		902	4.5 Other Types of Emotion Analysis
		903
		904	We have seen that sentiment analysis as applied to literature can be used for a
		905	number of downstream tasks, such as classification of texts based on the emotions
		906	they convey, genre classification based on emotions, and sentiment analysis in the
		907	historical domain. However, the application of sentiment analysis is not limited to
		908	these tasks. In this concluding part of the survey, we review some papers that do
		909	not
		910	formulate their approach to sentiment analysis as a downstream task. Often, the goal
		911	of these works is to understand how sentiments and emotions are represented in
		912	literary texts in general, and how sentiment or emotion content varies across
		913	specific documents or a collection of them with time, where time can be either
		914	relative to the text in question (from beginning to end) or to the historical changes
		915	in language (from past to present). Such information is valuable for gaining a deeper
		916	insight into how sentiments and emotions change over time, allowing us to bring
		917	forward new theories or shed more light onto existing literary or sociological
		918	theories.
		919
		920
		921	4.5.1 Emotion flow analysis and visualization
		922
		923	A set of authors aimed to visualize the change of emotion content through texts or
		924	across time. One of the earliest works in this direction is a paper by Anderson and
		925	McMaster[105] that starts from
		926	the premise that reading enjoyment stems from the affective tones of a text. These
		927	affective tones create a conflict that can rise to a climax through a series of
		928	crises, which is necessary for a work of fiction to be attractive to the reader.
		929	Using a list of 1,000 of the most common English words annotated with valence,
		930	arousal, and dominance ratings,[106] they
		931	calculate the conflict score by taking the mean of the ratings for each word in a
		932	text passage. The more negative the score is, the higher the conflict is, and vice
		933	versa. Additionally, they plot conflict scores for each consecutive 100 words of a
		934	test story and provide qualitative analysis of the peaks. They argue that a reader
		935	who has access to the text would be able to find correlation between events in the
		936	story and peaks on the graph. However, the authors still stress that such
		937	interpretation remains dependent upon the judgement of the reader. Further, other
		938	contributions by the authors are based on the same premises.[107]
		939	Alm and Sproat[108] present the results of
		940	the emotion annotation task of 22 tales by the Grimm brothers and evaluate patterns
		941	of emotional story development. They split emotions into positive and negative categories and divide each
		942	story into five parts from which aggregate frequency counts of combined emotion
		943	categories are computed. The resulting numbers are plotted on a graph that shows a
		944	wave-shaped pattern. From this graph, Alm and Sproat argue, one can see that the
		945	first part of the fairy tales is the least emotional, which is probably due to scene
		946	setting, while the last part shows an increase in positive emotions, which may
		947	signify the happy ending.
		948
		949	Two other studies by Mohammad[109] focus on differences in emotion word density as well as emotional
		950	trajectories between books of different genres. Emotion word density is defined as
		951	a
		952	number of times a reader will encounter an emotion word on reading every X words. In addition, each text is assigned several emotion
		953	scores for each emotion that are calculated as a ratio of words associated with one
		954	emotion to the total number of emotion words occurring in a text. Both metrics use
		955	the NRC Affective Lexicon to find occurrences of emotion
		956	words. They find that fairy tales have significantly higher anticipation, disgust, joy and
		957	surprise word densities, but lower trust word densities when compared to novels.
		958
		959	A work by Klinger et al.[110] is a case
		960	study in an automatic emotion analysis of Kafka’s Amerika and Das Schloss. The goal of the work is to analyze the development of emotions in both texts
		961	as well as to provide a character-oriented emotion analysis that would reveal
		962	specific character traits in both texts. To that end, Klinger et al. develop German
		963	dictionaries of words associated with Ekman’s fundamental emotions plus contempt and
		964	apply them to both texts in question to automatically detect emotion words. The
		965	results of their analysis for Das Schloss show a striking increase of surprise towards the end
		966	and a peak of fear shortly after start of chapter 3. In the
		967	case of Amerika, the analysis shows that there is a decrease in enjoyment after a peak in chapter 4.
		968
		969	Yet another work that tracks the flow of emotions in a collection of texts is
		970	presented by Kim et al.[111] The authors
		971	hypothesize that literary genres can be linked to the development of emotions over
		972	the course of text. To test this, they collect more than 2,000 books from five genres
		973	(adventure, science fiction, mystery, humor and romance) from Project Gutenberg and identify prototypical emotion shapes for
		974	each genre. Each novel in the corpus is split into five consecutive equally-sized
		975	segments (following the five-act theory of dramatic acts).[112] All five genres show close correspondence with regard to sadness, anger, fear and disgust, i.e., a consistent increase of
		976	these emotions from Act 1 to Act 5, which may correspond to an entertaining
		977	narrative. Mystery and science fiction
		978	books show increase in anger towards the end, and joy shows an inverse decreasing pattern from Act 1 to Act 2,
		979	with the exception of humor.
		980
		981	The work by Kakkonen and Galic Kakkonen[113] aims at supporting the literary analysis of Gothic texts at the sentiment level. The authors introduce a
		982	system called SentiProfiler that generates visual
		983	representations of affective content in such texts and outlines similarities and
		984	differences between them, however, without considering the temporal dimension. The
		985	SentiProfiler uses WordNet-Affect to
		986	derive a list of emotion-bearing words that will be used for analysis. The resulting
		987	sentiment profiles for the books are used to visualize the presence of sentiment in
		988	a
		989	particular document and to compare two different texts.
		990
		991
		992
		993	4.5.2 Miscellaneous
		994
		995	In this section, we review studies that are different in goals and research questions
		996	from the papers presented in previous sections and do not constitute a category on
		997	their own.
		998
		999	Koolen[114] claims that there is a bias among
		1000	readers that put works by female authors on par with »women’s books«, which, as
		1001	stated by the author, tend to be perceived as of lower literary quality. She
		1002	investigates how much »women’s books« (here, romantic novels
		1003	written by women) differ from novels perceived as literary (female and male-authored
		1004	literary fiction). The corpus used in the study is a collection of European and
		1005	North-American novels translated into Dutch. Koolen uses a Dutch version of the Linguistic Inquiry and Word Count,[115] a dictionary that contains content and sentiment-related categories
		1006	of words to count the number of words from different categories in each type of
		1007	fiction. Her analysis shows that romantic novels contain more positive emotions and
		1008	words pertaining to friendship than in literary fiction. However, female-authored
		1009	literary novels and male-authored ones do not significantly differ on any category.
		1010
		1011
		1012	Kraicer and Piper[116] explore the
		1013	women’s place within contemporary fiction starting from the premise that there is
		1014	a
		1015	near ubiquitous underrepresentation and decentralization of women. As a part of their
		1016	analysis, Kraicer and Piper use sentiment scores to look at social balance and
		1017	»antagonism«, i.e., how different gender pairings influence positive and negative
		1018	language surrounding the co-occurrence of characters (using the sentiment dictionary
		1019	presented by Liu[117] to calculate a
		1020	sentiment score for a character pair). Having analyzed a set of 26,450 characters
		1021	from 1,333 novels published between 2001 and 2015, the authors find that sentiment
		1022	scores give little indication that the character’s gender has an effect on the state
		1023	of social balance.
		1024
		1025	Morin and Acerbi[118] focus on
		1026	larger-scale data spanning a hundred thousand of books. The goal of their study is
		1027	to
		1028	understand how emotionality of written texts changed throughout the centuries. Having
		1029	collected 307,527 books written between 1900 and 2000 from the Google Books
		1030	corpus[119] they collect, for each
		1031	year, the total number of case-insensitive occurrences of emotion terms that are
		1032	found under positive and negative taxonomies of LIWC
		1033	dictionary.[120] The main findings
		1034	of their research show that emotionality (both positive and
		1035	negative emotions) declines with time, and this decline is
		1036	driven by the decrease in usage of positive vocabulary. Morin and Acerbi remind us
		1037	that the Romantic period was dominated by emotionality in
		1038	writing, which could be the effect of a group of writers who wrote above the mean.
		1039	If
		1040	one assumes that each new writer tends to copy the emotional style of their
		1041	predecessors, then writers at one point of time are disproportionally influenced by
		1042	this group of above-the-mean writers. However, this trend does not last forever and,
		1043	sooner or later, the trend reverts to the mean, as each writer reverts to a normal
		1044	level of emotionality.
		1045
		1046	An earlier work[121] written in
		1047	collaboration with Acerbi provides a somewhat different
		1048	approach and interpretation of the problem of the decline in positive vocabulary in
		1049	English books of the twentieth century. Using the same dataset and lexical resources
		1050	(plus WordNet-Affect) Bentley et al. find a strong correlation
		1051	between expressed negative emotions and the U.S. economic misery
		1052	index, which is especially strong for the books written during and after
		1053	the World War I (1918), the Great Depression (1935), and the energy crisis (1975).
		1054	However, in the present study,[122] the
		1055	authors argue that the extent to which positive emotionality correlates with
		1056	subjective well-being is a debatable issue. Morin and Acerbi provide more possible
		1057	reasons for this effect as well as detailed statistical analysis of the data, so we
		1058	refer the reader to the original paper for more information.
		1059
		1060
		1061
		1062	Tab. 1: Summary of characteristics of methods used in the papers reviewed
		1063	in this survey. Download as PDF. [Kim / Klinger 2019]
		1064
		1065
		1066
		1067
		1068
		1069	5 Discussion and Conclusion
		1070
		1071	We have shown throughout this survey that there is a growing interest in sentiment
		1072	and emotion analysis within digital humanities. Given the fact that DH have emerged
		1073	into a thriving science within the past decade, it may safely be said that this
		1074	direction of research is relatively new. At the same time, the research in sentiment
		1075	analysis started in computational linguistic more than two decades ago and is
		1076	nowadays an established field that has dedicated workshops and tracks in the main
		1077	computational linguistics conferences. Moreover, a recent meta-study by Mäntylä et
		1078	al.[123] shows that the number of
		1079	papers in sentiment analysis is rapidly increasing each year. Indeed, the topic has
		1080	not yet outrun itself and we should not expect to see it vanishing within the next
		1081	decade or two, provided that no significant paradigm shift in the computational
		1082	sciences takes place. One may wonder whether the same applies to sentiment analysis
		1083	in digital humanities scholarship. Will the interest in the topic grow continuously
		1084	or will it rally to the peak and vanish in a few years?
		1085
		1086	There is no decisive answer. The popularity of sentiment analysis may have reached
		1087	a
		1088	peak but is far from fading. Application-wise, not a lot has changed during the past
		1089	years: researchers are still interested in predicting sentiment and emotion from text
		1090	for different purposes. If anything has changed, it is methodology. Early research
		1091	in
		1092	sentiment analysis relied on word polarity and specific dictionaries. Modern
		1093	state-of-the-art approaches rely on word embeddings and deep learning architectures.
		1094	Having started with simple polarity detection, contemporary sentiment analysis has
		1095	advanced to a more nuanced analysis of sentiments and emotions.
		1096
		1097	The situation is somewhat different in digital humanities research. Most of the works
		1098	rely on affective lexicons and word counts, a technique for detecting emotions in
		1099	literary text first used by Anderson and McMaster in 1982.[124] Even the most recent works base the
		1100	interpretation of the results on the use of dictionaries and counts of
		1101	emotion-bearing words in a text, passage, or sentence. In fact, around 70% of the
		1102	papers we discussed in Section 4 substantially rely on the use of various lexical
		1103	resources for detecting emotions (see Table 1 for a summary of methods used in the
		1104	reviewed papers). We have discussed some limitations of this approach in Section 4.2.
		1105	Let us reiterate its weakness with the following small example. Consider the sentence
		1106	›Jack was afraid of John because John held a knife in his hand‹. Assuming a
		1107	dictionary of emotion-bearing words is used, the sentence can be categorized as
		1108	expressing fear, because of the two strong fear markers, afraid and knife. Indeed, the sentence
		1109	does express fear. But does it do it equally for Jack and
		1110	John? The answer is no: Jack is the one who is afraid and John holding a knife is
		1111	the
		1112	reason for Jack being afraid. Let us assume that a researcher is interested in the
		1113	emotion analysis of a book that contains thousands of sentences expressing emotions
		1114	in different ways: some sentences describe characters who feel emotions just as in
		1115	the sentence above, some are narrator’s digressions filled with emotions, some
		1116	contain emotion-bearing words (knife, baby) but do not in fact express the same emotion in any given context. No
		1117	doubt, a dictionary and count-based approach will be helpful in understanding the
		1118	distribution of the emotion lexicon throughout the story. But is it enough for the
		1119	interpretation? Can hermeneutics, in its traditional form, make use of such
		1120	knowledge? Barely. In fact, some of the works that we reviewed pinpoint that the
		1121	surface affective value of the words does not always align with their more nuanced
		1122	affective meaning and that sentiment analysis tools make mistakes when classifying
		1123	a
		1124	text as emotional or not.[125] If so, how reliable
		1125	is the interpretation? In other words, what kind of interpretation should we expect
		1126	from the sentiment and emotion analysis research in the DH community?
		1127
		1128	We do not have a ready answer to that question. At the one extreme, there is
		1129	traditional hermeneutics, the examples of which are presented in a Section 3. At the
		1130	other extreme, there is interpretation in the form of ›Author A writes with more
		1131	emotion than author B because the numbers say so‹. We do, however, suggest that a
		1132	balance should be made somewhere between these two extremes. Even as simple as it
		1133	is,
		1134	the approach of detecting sentiment and emotion-related words can be used to deliver
		1135	a high-quality interpretation such as in Heuser et al.[126] or Morin and Acerbi.[127] However, we note again that there are still limits posed by the
		1136	simplicity of this approach.
		1137
		1138	This leads us to an outline of the reality of sentiment analysis research in digital
		1139	humanities: the methods of sentiment analysis used by some of the DH scholars
		1140	nowadays have gone or are almost extinct among computational linguists. This in turn
		1141	affects the quality of the interpretation.
		1142
		1143	However, we admit that this criticism may be unfair. In fact, there is a possible
		1144	reason why DH researchers have taken this approach to sentiment analysis. Digital
		1145	humanities are still being formed as an independent discipline and it is easier to
		1146	form something new in a step-by-step fashion. Resorting to a metaphor from the
		1147	construction world, one should first learn how to stack single bricks to build a wall
		1148	rather than starting from the design of a communications system. It is necessary to
		1149	make sure that appropriate tools and methods are chosen instead of using what proved
		1150	to be successful in other domains without reflection. It is true that much digital
		1151	humanities research (especially dealing with text) uses the methods of text analysis
		1152	that were in fashion in computational linguistic twenty years ago. One may argue that
		1153	new research in digital humanities should start with the state-of-the-art methods. Indeed, some arguments that methodology is at
		1154	the root of the interpretation have already been made.[128] So, if there is anything that digital humanities can learn from
		1155	computational linguistics, it is that methodology cannot stall. What really matters
		1156	for digital humanities is interpretation, and if methodology is not going forward,
		1157	the interpretation is not either.
		1158
		1159
		1160
		1161	Acknowledgements
		1162
		1163
		1164	We thank Laura Ana Maria Bostan, Sebastian Padó, and Enrica Troiano
		1165	for fruitful discussions and the ZfDG team for their help in preparation of this
		1166	article. This research has been conducted within the CRETA project which is funded by the German Ministry for Education and
		1167	Research (BMBF) and partially funded by the German Research Council (DFG), projects
		1168	SEAT (Structured Multi-Domain Emotion Analysis from Text, KL 2869/1-1).
		1169
		1170
		1171
		1172
		1173
		1174	Footnotes
		1175
		1176
		1177	[1]
		1178
		1179	Liu 2015, p.2.
		1180
		1181
		1182	[2]
		1183
		1184	Soleymani et al. 2017.
		1185
		1186
		1187	[3]
		1188
		1189	Scherer 2005, p. 695.
		1190
		1191
		1192	[4]
		1193
		1194	Scarantino 2016, p. 36.
		1195
		1196
		1197	[5]
		1198
		1199	Mayer et al. 2008, p. 510.
		1200
		1201
		1202	[6]
		1203
		1204	Da 2019, p. 602.
		1205
		1206
		1207	[7]
		1208
		1209	Moretti 2005.
		1210
		1211
		1212	[8]
		1213
		1214	Hoover et al. 2014.
		1215
		1216
		1217	[9]
		1218
		1219	Schwarz 2000, p. 433.
		1220
		1221
		1222	[10]
		1223
		1224	Johnson-Laird / Oatley 2016,
		1225	passim; Djikic et al. 2009, passim.
		1226
		1227
		1228	[11]
		1229
		1230	Robinson 2005;
		1231	Hogan 2010;
		1232	Hogan 2011;
		1233	Bal / Veltkamp 2013;
		1234	Djikic et al. 2013;
		1235	Johnson 2012;
		1236	Samur et al. 2018.
		1237
		1238	[12]
		1239
		1240	Zillmann et al. 1980;
		1241	Ross 1999;
		1242	Bryant / Zillmann 1984;
		1243	Oliver 2008;
		1244	Mar et al.
		1245	2011.
		1246
		1247	[13]
		1248
		1249	Plato 1969
		1250	, passim.
		1251
		1252
		1253	[14]
		1254
		1255	Aristotle 1996, passim.
		1256
		1257
		1258	[15]
		1259
		1260	De Sousa / Scarantino 2018.
		1261
		1262
		1263	[16]
		1264
		1265	Tolstoy 1962, passim.
		1266
		1267
		1268	[17]
		1269
		1270	Anderson / McMaster 1986, p. 3;
		1271	Hogan 2010, p. 187; Piper /
		1272	Jean So 2015.
		1273
		1274
		1275	[18]
		1276
		1277	Lanham 1989.
		1278
		1279
		1280	[19]
		1281
		1282	Berry 2012; Schreibman et al. 2015.
		1283
		1284
		1285	[20]
		1286
		1287	Vanhoutte 2013, p. 142;
		1288	Jockers / Underwood
		1289	2016, pp. 292f.
		1290
		1291
		1292	[21]
		1293
		1294	Anderson /
		1295	McMaster 1982.
		1296
		1297
		1298	[22]
		1299
		1300	Darwin 1872, passim.
		1301
		1302
		1303	[23]
		1304
		1305	Gendron / Feldman Barrett 2009.
		1306
		1307
		1308	[24]
		1309
		1310	Tomkins 1962, passim.
		1311
		1312
		1313	[25]
		1314
		1315	Ekman et al. 1969, pp. 86-88.
		1316
		1317
		1318	[26]
		1319
		1320	Ekman 1993, p. 386.
		1321
		1322
		1323	[27]
		1324
		1325	Feldman Barrett 1998, pp. 580f.
		1326
		1327
		1328	[28]
		1329
		1330	Russell 1994;
		1331	Russell et al. 2003;
		1332	Gendron et al. 2014;
		1333	Feldman Barrett 2017.
		1334
		1335
		1336	[29]
		1337
		1338	Plutchik 1991, passim.
		1339
		1340
		1341	[30]
		1342
		1343	Cambria et al. 2012;
		1344	Kim et al. 2012; Suttles / Ide 2013;
		1345	Borth et al. 2013; Abdul-Mageed /
		1346	Ungar 2017.
		1347
		1348
		1349	[31]
		1350
		1351	Smith / Schneider 2009, passim.
		1352
		1353
		1354	[32]
		1355
		1356	Richins 1997, p. 128.
		1357
		1358
		1359	[33]
		1360
		1361	Russell 1980.
		1362
		1363
		1364	[34]
		1365
		1366	Bradley / Lang 1994, p. 50.
		1367
		1368
		1369	[35]
		1370
		1371	Russell 2003, p. 154.
		1372
		1373
		1374	[36]
		1375
		1376	Larsen / Diener 1992, p. 25.
		1377
		1378
		1379	[37]
		1380
		1381	Russell / Feldman Barrett 1999, p. 807.
		1382
		1383
		1384	[38]
		1385
		1386	Sætre
		1387	et al. 2014b, passim.
		1388
		1389
		1390	[39]
		1391
		1392	Van Meel 1995, passim.
		1393
		1394
		1395	[40]
		1396
		1397	Kuivalainen 2009, passim.
		1398
		1399
		1400	[41]
		1401
		1402	Barton 1996, passim.
		1403
		1404
		1405	[42]
		1406
		1407	Van Horn
		1408	1997, passim.
		1409
		1410
		1411	[43]
		1412
		1413	Johnson-Laird / Oatley 1989, passim.
		1414
		1415
		1416	[44]
		1417
		1418	Miller 2014, p. 92.
		1419
		1420
		1421	[45]
		1422
		1423	Sætre et al. 2014a, p. 91ff.
		1424
		1425
		1426	[46]
		1427
		1428	Miller 2014, p.
		1429	93.
		1430
		1431
		1432	[47]
		1433
		1434	Miller 2014, p. 115.
		1435
		1436
		1437	[48]
		1438
		1439	Liu 2015, p. 47.
		1440
		1441
		1442	[49]
		1443
		1444	Barros et al. 2013, passim.
		1445
		1446
		1447	[50]
		1448
		1449	Reed 2018, passim.
		1450
		1451
		1452	[51]
		1453
		1454	Yu 2008, passim.
		1455
		1456
		1457	[52]
		1458
		1459	Zehe et al. 2016, passim.
		1460
		1461
		1462	[53]
		1463
		1464	Mohammad / Turney 2013, passim.
		1465
		1466
		1467	[54]
		1468
		1469	Reagan et al.
		1470	2016, passim.
		1471
		1472
		1473	[55]
		1474
		1475	Vonnegut 2010 (2005), passim.
		1476
		1477
		1478	[56]
		1479
		1480	Project Gutenberg 1971-2019.
		1481
		1482
		1483	[57]
		1484
		1485	Samothrakis / Fasli 2015;
		1486	Kim et al.
		1487	2017a; Kim et al. 2017b.
		1488
		1489
		1490	[58]
		1491
		1492	Strapparava / Valitutti 2004.
		1493
		1494
		1495	[59]
		1496
		1497	Kim et al. 2017a, passim.
		1498
		1499
		1500	[60]
		1501
		1502	Francis / Kucera 1979, passim.
		1503
		1504
		1505
		1506	[61]
		1507
		1508	Henny-Krahmer 2018, passim.
		1509
		1510
		1511	[62]
		1512
		1513	Baccianella et al. 2010.
		1514
		1515
		1516
		1517	[63]
		1518
		1519	Mohammad / Turney 2013.
		1520
		1521
		1522	[64]
		1523
		1524	Heuser et al. 2016, passim.
		1525
		1526
		1527	[65]
		1528
		1529	Historypin 2010-2017.
		1530
		1531
		1532	[66]
		1533
		1534	Bruggmann / Fabrikant 2014, passim.
		1535
		1536
		1537	[67]
		1538
		1539	Stone et al. 1968.
		1540
		1541
		1542	[68]
		1543
		1544	Taboada et al. 2006, passim; Taboada et al. 2008, passim.
		1545
		1546
		1547	[69]
		1548
		1549	Chen et al. 2012, passim.
		1550
		1551
		1552	[70]
		1553
		1554
		1555	Strapparava / Valitutti 2004.
		1556
		1557
		1558	[71]
		1559
		1560	Oceanic Exchanges 2017.
		1561
		1562
		1563	[72]
		1564
		1565	Marchetti et al. 2014, passim.
		1566
		1567
		1568	[73]
		1569
		1570	Sprugnoli et al. 2016, passim.
		1571
		1572
		1573	[74]
		1574
		1575	ALCIDE Demo 2014-2015.
		1576
		1577
		1578	[75]
		1579
		1580	Baccianella et al. 2010, passim.
		1581
		1582
		1583	[76]
		1584
		1585	Pianta et al. 2002, passim.
		1586
		1587
		1588	[77]
		1589
		1590	Buechel et al. 2017, passim.
		1591
		1592
		1593	[78]
		1594
		1595	Buechel et al. 2016, p. 54, p. 59.
		1596
		1597
		1598	[79]
		1599
		1600	Deutsches Textarchiv 2007-2019.
		1601
		1602
		1603	[80]
		1604
		1605	Leemans et al. 2017, passim.
		1606
		1607
		1608	[81]
		1609
		1610	Pennebaker et al. 2007.
		1611
		1612
		1613	[82]
		1614
		1615	Ingermanson / Economy 2009, p.
		1616	107.
		1617
		1618
		1619	[83]
		1620
		1621	Agarwal et al. 2013;
		1622	Elson et al. 2011.
		1623
		1624
		1625	[84]
		1626
		1627	Nalisnick / Baird 2013a, passim.
		1628
		1629
		1630	[85]
		1631
		1632	Nielsen 2011, passim.
		1633
		1634
		1635	[86]
		1636
		1637	Nalisnick / Baird 2013b, passim.
		1638
		1639
		1640	[87]
		1641
		1642	Marvel et al. 2011.
		1643
		1644
		1645	[88]
		1646
		1647	Elsner 2012, passim;
		1648	Elsner 2015, passim.
		1649
		1650
		1651	[89]
		1652
		1653	Kim / Klinger 2018, passim.
		1654
		1655
		1656	[90]
		1657
		1658	REMAN - Relational Emotion Annotation for Fiction. Corpus 2018.
		1659
		1660
		1661	[91]
		1662
		1663	Barth et al. 2018, passim.
		1664
		1665
		1666	[92]
		1667
		1668	Jhavar / Mirza
		1669	2018, passim.
		1670
		1671
		1672	[93]
		1673
		1674	EMoFiel: Emotion Mapping of Fictional Relationship 2018.
		1675
		1676
		1677	[94]
		1678
		1679	Plutchik 1991, passim.
		1680
		1681
		1682	[95]
		1683
		1684	Russell 1980, passim.
		1685
		1686
		1687	[96]
		1688
		1689	Egloff et al. 2018, passim.
		1690
		1691
		1692	[97]
		1693
		1694	Patti et al. 2015.
		1695
		1696
		1697	[98]
		1698
		1699	Cambria et al. 2012, passim.
		1700
		1701
		1702	[99]
		1703
		1704	Kim / Klinger 2019b, passim.
		1705
		1706
		1707	[100]
		1708
		1709	Kim / Klinger
		1710	2019a, passim.
		1711
		1712
		1713	[101]
		1714
		1715	Their
		1716	analysis is based on Van Meel 1995 we mentioned in
		1717	Section 3.
		1718
		1719
		1720	[102]
		1721
		1722	Rinaldi et al. 2013, passim.
		1723
		1724
		1725	[103]
		1726
		1727	Zhuravlev et al. 2014, passim.
		1728
		1729
		1730	[104]
		1731
		1732	Jafari et al. 2016, passim.
		1733
		1734
		1735	[105]
		1736
		1737	Anderson / McMaster 1986, passim.
		1738
		1739
		1740	[106]
		1741
		1742	Heise 1965, passim.
		1743
		1744
		1745	[107]
		1746
		1747	Anderson / McMaster 1982;
		1748	Anderson / McMaster 1993.
		1749
		1750
		1751	[108]
		1752
		1753	Alm / Sproat 2005, passim.
		1754
		1755
		1756	[109]
		1757
		1758	Mohammad 2011, passim;
		1759	Mohammad
		1760	2012, passim.
		1761
		1762
		1763	[110]
		1764
		1765	Klinger et al. 2016, passim.
		1766
		1767
		1768	[111]
		1769
		1770	Kim et al. 2017b, passim.
		1771
		1772
		1773	[112]
		1774
		1775	Freytag 1863, passim.
		1776
		1777
		1778	[113]
		1779
		1780	Kakkonen /
		1781	Galic Kakkonen 2011, passim.
		1782
		1783
		1784	[114]
		1785
		1786	Koolen 2018, passim.
		1787
		1788
		1789	[115]
		1790
		1791	Boot et al.
		1792	2017.
		1793
		1794
		1795	[116]
		1796
		1797	Kraicer / Piper 2019, passim.
		1798
		1799
		1800	[117]
		1801
		1802	Liu et al. 2010, passim.
		1803
		1804
		1805	[118]
		1806
		1807	Morin / Acerbi 2017, passim.
		1808
		1809
		1810	[119]
		1811
		1812	Google Books Ngram Viewer 2012.
		1813
		1814
		1815	[120]
		1816
		1817	Pennebaker et al. 2007.
		1818
		1819
		1820	[121]
		1821
		1822	Bentley et al. 2014, passim.
		1823
		1824
		1825	[122]
		1826
		1827	Morin / Acerbi 2017, passim.
		1828
		1829
		1830	[123]
		1831
		1832	Mäntylä et al. 2018, passim.
		1833
		1834
		1835	[124]
		1836
		1837	Anderson / McMaster 1982, passim.
		1838
		1839
		1840	[125]
		1841
		1842	Reed 2018, passim.
		1843
		1844
		1845	[126]
		1846
		1847	Heuser
		1848	et al. 2016, passim.
		1849
		1850
		1851	[127]
		1852
		1853	Morin and Acerbi
		1854	2017, passim.
		1855
		1856
		1857	[128]
		1858
		1859	Da
		1860	2019, passim.
		1861
		1862
		1863
		1864
		1865
		1866
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		2301
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		2303	List of Figures with Captions
		2304
		2305
		2306	Fig. 1: Plutchik’s wheel of emotions. [Plutchik 2011.
		2307	PD]
		2308
		2309
		2310	Fig. 2: Circumplex model of affect: Horizontal axis represents the valence dimension,
		2311	the vertical axis represents the arousal dimension. Drawn after Posner et al. 2005. [Kim / Klinger 2019]
		2312
		2313
		2314	Tab. 3: Summary of characteristics of methods used in the papers reviewed
		2315	in this survey. Download as PDF. [Kim / Klinger 2019]
		2316
18		2317

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