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3	Evgeny Kim	4	Evgeny Kim
4	Kontakt: evgeny.kim@ims.uni-stuttgart.deInstitution: Universität Stuttgart, Institut für Maschinelle Sprachverarbeitung GND: 1193672481ORCID: 0000-0001-6822-6709	5	Kontakt: evgeny.kim@ims.uni-stuttgart.deInstitution: Universität Stuttgart, Institut für Maschinelle
		6	Sprachverarbeitung GND: 1193672481ORCID: 0000-0001-6822-6709
5	Roman Klinger	7	Roman Klinger
6	Kontakt: roman.klinger@ims.uni-stuttgart.deInstitution: Universität Stuttgart, Institut für Maschinelle Sprachverarbeitung GND: 173873820ORCID: 0000-0002-2014-6619	8	Kontakt: roman.klinger@ims.uni-stuttgart.deInstitution: Universität Stuttgart, Institut für Maschinelle
		9	Sprachverarbeitung GND: 173873820ORCID: 0000-0002-2014-6619
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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
12	Erstveröffentlichung: 16.12.2019	15	Erstveröffentlichung: 16.12.2019
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14	Lizenz: Sofern nicht anders angegeben	17	Version 2.0: 23.07.2021
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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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63	Zu diesem Artikel ist eine überarbeitete Version erschienen: Version 2
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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
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2299
2300
2301
2302
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
2317		18

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