4.1.1 Classification based on emotions

[36]Barros et al.[54] aim at answering two research questions: 1) is the classification of Francisco de Quevedo’s works proposed by the literary scholars consistent with the sentiment reflected by the corresponding poems; and 2) which learning algorithms are the best for the classification (the latter being an engineering question that is inherent in many of the papers that we discuss)? They perform a set of experiments on the classification of 185 Francisco de Quevedo’s poems that are divided by literary scholars into four categories and that Barros et al. map to emotions. Using the terms joy, anger, fear, and sadness as points of reference, Barros et al. construct a list of emotion words by looking up the synonyms of English emotion words and adjectives associated with these four emotions and translating them into Spanish. This leads to a novel and task-specific lexicon, to which each poem is then compared, based on normalized term counts. The experiments show the superiority of decision trees as classification approach which can further be improved by rebalancing the collection via resampling. Based on these results the authors conclude that a meaningful classification of the literary pieces based only on the emotion information is possible.

[37]A more modern corpus selection of poetry is the object of analysis by Ethan Reed.[55]. The author offers a proof-of-concept for performing sentiment analysis on twentieth-century American poetry with dictionary-based black-box sentiment analysis systems that output the polarity of a text. Specifically, they analyze the expression of emotions in the poetry of the Black Arts Movement of the 1960s and 1970s. The goal of the project is to understand how feelings associated with injustice are coded in terms of race and gender, and what sentiment analysis can show us about the relations between affect and gender in poetry. Reed notes that the surface affective value of the words does not always align with their more nuanced affective meaning shaped by poetic, social, and political contexts. Therefore, this study can be seen as a critical reflection on methodological choices.

[38]Yu[56] explores linguistic patterns that characterize the genre of sentimentalism in early American novels. They analyze five novels from the mid-nineteenth century and annotate the emotionality of each of the chapters as high or low (not: positive or negative!). This approach is noteworthy, as the unit of analysis is comparably large in contrast to most sentiment analysis methods. Each chapter is classified with standard configurations of support vector machines and naïve Bayes classifiers, as highly emotional or the opposite. The results of the evaluation suggest that arbitrary feature reduction steps such as stemming and stopword removal should be taken very carefully, as they may affect the prediction.

[39]Volkova[57] did not focus on the classification of emotions automatically, but tackles the task of annotation in more detail. The authors observe that annotation of literature, in their case fairy tales, is challenging, and that it is hard to obtain an acceptable annotation agreement. An interesting innovative element in this study is that annotators were not presented a predefined unit to annotate – they were allowed to decide by themselves which granularity is most reasonable. That is different to the other studies mentioned before in this section. Further, a main finding was that short instances lead to a lower agreement.

[40]Finally, an interesting study by Ashok et al.[58] did not classify emotions regarding a variable motivated by literary studies. They use sentiment polarity as one component to predict the success of a book. While such studies (similarly the prediction of citation counts, etc.) are often criticized, the authors present some interesting, but also perhaps non-surprising findings, e.g. that unsuccessful stories contain more discriminative words that have a negative connotation.

4.1.2 Classification of happy ending vs. non-happy endings

[41]A particular use case of emotion classification is to look closer at particular parts of a text. Zehe et al.[59] argue that automatically recognizing a happy ending as a major plot element could help to better understand a plot structure as a whole. To show that this is possible, they classify 212 German novels written between 1750 and 1920 as having happy or non-happy endings. A novel is considered to have a happy ending if the situation of the main characters in the novel improves towards the end or is constantly favorable. The novels were manually annotated with this information by domain experts. For feature extraction, the authors first split each novel into n segments of the same length. They then calculate sentiment values for each of the segments based on a normalized word frequency with a German version of the NRC Word-Emotion Association Lexicon. [60] An automatic sentiment classification with support vector machines achieves reasonable and encouraging results.

4.2.1 Story-type clustering

[43]Similarly to Zehe et al., Reagan et al.[61] are interested in automatically understanding a plot structure as a whole, but not limited to a book ending. The inspiration for their work comes from Kurt Vonnegut’s lecture on emotional arcs of stories.[62] Reagan et al. test the idea that the plot of each story can be visualized as an emotional arc, i.e., a time series graph, where the x-axis represents a time point in a story, and the y-axis represents the events happening to the main characters that can be favorable (peaks on a graph) or unfavorable (troughs on a 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 et al. collect the 1,327 most popular books from the Project Gutenberg.[63] Each book is then split into segments for which happiness scores are calculated and compared. The results of the analysis show support for six emotional patterns that are shared between subgroupings of the corpus. Additionally, Reagan et al. find that some patterns are more popular among readers, based on download counts, than others.

4.2.2 Genre classification

[44]There are other studies[64] that are similar in spirit to the work done by Reagan et al. Samothrakis and Fasli examine the hypothesis that different genres clearly have different emotion patterns to reliably classify them with machine learning. To that end, they collect works of the genres mystery, humor, fantasy, horror, science fiction and western from the Project Gutenberg. Using WordNet-Affect[65] to detect emotion words as categorized by Ekman’s fundamental emotion classes, they calculate an emotion score for each sentence in the text. Each work is then transformed into six vectors, one for each basic emotion. With a random forrest classifier, they show that genre classification is possible based on this information with performance scores significantly above average.

[45]The study by Kim et al.[66] originates from the same premise as the work by Samothrakis and Fasli but puts emphasis on finding genre-specific correlations of emotion developments. They therefore link the motivation of Reagan et al. with the one by Samothrakis and Fasli. Extending the set of tracked emotions to Plutchik’s classification, Kim et al. collect 2,000 books from the Project Gutenberg that belong to five genres found in the Brown corpus,[67] namely adventure, science fiction, mystery, humor and romance. The authors extend the set of classification algorithms beyond random forests using a multi-layer perceptron and convolutional neural networks, which achieves the best performance. To understand how uniform the emotion patterns in different genres are, the authors introduce the notion of prototypicality, which is computed as average of all emotion scores. Using this as a point of reference for each genre Kim et al. use Spearman correlation to calculate the uniformity of emotions per genre. 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 findings of Samothrakis and Fasli.

[46]The study by Henny-Khramer[68] pursues two goals: 1), to test whether different subgenres of Spanish American literature differ in degree and kind of emotionality, and 2), whether emotions in the novels are expressed in direct speech of characters or in narrated text. To that end, they conduct a subgenre classification experiment on a corpus of Spanish American novels using sentiment values as features. To answer the first question, each novel is split into five segments and for each sentence in the segment the emotion score (polarity values + Plutchik’s basic emotions) is calculated using SentiWordNet[69] and NRC[70] dictionaries. The analysis of feature importance shows that the most salient features come from the sentiment scores calculated from the characters’ direct speech and that novels with higher values of positive speech are more likely to be sentimental novels. This is an interesting variant of the beforehand mentioned studies – it is important to distinguish characters' speech from other parts of the text.

[47]There are some limitations to the studies presented in this section. On the one hand, it is questionable how reliable coarse emotion scoring is that takes into account only presence or absence of words found in specialized dictionaries and overlooks negations and modifiers that can either negate an emotion word or increase/decrease its intensity. On the other hand, a limited view of the emotional content as a sum of emotion bearing words reserves no room for qualitative interpretation of the texts – it is not clear how one can distinguish between emotion words used by the author to express their sentiment, between words used to describe characters’ feelings, and emotion words that characters use to address or describe other characters in a story.

4.3.1 Topography of emotions

[49]Heuser et al.[71] start with a premise that emotions occur at a specific moment in time and space, thus making it possible to link emotions to specific geographical locations. Consequently, having such information at hand, one can understand which emotions are hidden behind certain landmarks. As a proof-of-concept, Heuser et al. build an interactive map of emotions in Victorian London[72] where each location is tagged with emotion labels. The underlying corpus for their analysis consists of English books from the eighteenth and nineteenth century, from which they extract frequently mentioned geographical locations of London. The presegmented data is then given to annotators who are asked to define whether each of the passages expressed happiness or fear, or neutrality. The same data is further analyzed with a dictionary-based sentiment classifier.

[50]Some striking observations are made with regard to the data analysis. First, there is a clear discrepancy between fiction and reality – while toponyms from the West End with Westminster and the City are over-represented in the books, the same does not hold true for the East End with Tower Hamlets, Southwark, and Hackney. Hence, there is less information about emotions pertaining to these particular London locations. Another striking detail is that the resulting map is dominated by the neutral emotion. Heuser et al. argue that this has nothing to do with the absence of emotions but rather stems from the fact that emotions tend to be silenced in public domain, which influenced the annotators decision.

[51]The space and time context are also used by Bruggman and Fabrikant[73] who model sentiments of Swiss historians towards places in Switzerland in different historical periods. As the authors note, it is unlikely that a historian will directly express attitudes towards certain toponyms, but it is very likely that words they use to describe those can bear some negative connotation (e.g. cholera, death). Correspondingly, such places should be identified as bearing negative sentiment by a sentiment analysis tool. Additionally, they study the changes of sentiment towards a particular place over time. Using the General Inquirer (GI) lexicon[74] to identify positive and negative terms in the document, they assign sentiment scores and conclude that the results of their analysis look promising, especially regarding negatively scored articles.

4.3.2 Tracking sentiment

[52]Other papers in this category link sentiment and emotion to certain groups, rather than geographical locations. The goal of these studies is to understand how sentiment within and towards these groups was formed.

[53]Taboada et al.[75] aim at tracking the literary reputation of six authors writing in the first half of the twentieth century. The research questions raised in the project are how the reputation is made or lost, and how to find correlation between what is written about the authors and their work to the authors’ reputation and subsequent canonicity. The project’s goal is to examine critical reviews of six authors’ writing and to map information contained in texts critical to the author’s reputation. The material they work with includes not only reviews, but also press notes, press articles, and letters to editors (including from the authors themselves). They collected and scanned 330 documents and tagged them with polarity words with custom-made sentiment dictionaries. The sentiment orientation of rhetorically important parts of the texts is then measured. The authors conclude that the current approach has mostly been limited by a non-sufficiently large lexicon.

[54]Chen et al.[76] aim to understand personal narratives of Korean comfort women who had been forced into sexual slavery by Japanese military during World War II. Adapting the WordNet-Affect lexicon,[77] Chen et al. build their own emotion dictionary to spot keywords in women’s stories and map the sentences to emotion categories. By adding variables of time and space, Chen et al. provide a unified framework of collective remembering of this historical event as witnessed by the victims.

[55]An interesting methodological contribution has been made by Gao et al.[78] Instead of using raw counts of polarity words over time, they propose that filters are used to smooth the time series, which further allows for other downstream applications.

4.3.3 Sentiment recognition in historical texts

[56]Other papers put emphasis not so much on the sentiments expressed by writers but instead focus on the particularities of historical language. Marchetti et al.[79] and Sprugnoli et al.[80] present the integration of sentiment analysis in the ALCIDE (Analysis of Language and Content in a Digital Environment) project.[81] The sentiment analysis module is based on WordNet-Affect, SentiWordNet[82] and MultiWordNet.[83] Each document is assigned a normalized polarity score. The overall conclusion of their work is that the assignment of a polarity in the historical domain is a challenging task largely due to lack of agreement on polarity of historical sources between human annotators.

[57]Challenged by the problem of applicability of existing emotion lexicons to historical texts, Buechel et al.[84] propose a new method of constructing affective lexicons that would adapt well to German texts written up to three centuries ago. In their study, Buechel et al. use the representation of affect based on the Valence-Arousal-Dominance model (an adaptation of Russel’s circumplex model, see section 2.3). Presumably, such a representation provides a finer-grained insight into the literary text,[85], which is more expressive than discrete categories, as it quantifies the emotion along three different dimensions. As a basis for the analysis, they collect German texts from the Deutsches Textarchiv[86] written between 1690 and 1899. The corpus is split into seven slices, each spanning 30 years. For each slice they compute word similarities and obtain seven distinct emotion lexicons, each corresponding to specific time period. This allows for, the authors argue, the tracing of the shift in emotion association of words over time.

[58]Finally, Leemans et al.[87] aim to trace historical changes in emotion expressions and to develop methods to trace these changes in a corpus of 29 Dutch language theatre plays written between 1600 and 1800. Expanding the Dutch version of Linguistic Inquiry and Word Count (LIWC) dictionary[88] with historical terms, the authors are able to increase the recall of emotion recognition with a dictionary. In addition, they develop a fine-grained vocabulary mapping body terms to emotions, and show that a combination of LIWC and their lexicon lead to improvement in the emotion recognition.

4.4.1 Sentiment dynamics between characters

[61]Several studies present automatic methods for analyzing sentiment dynamics between plays’ characters. The goal of the study by Nalisnick and Baird[91] is to track the emotional trajectories of interpersonal relationships. The structured format of a dialog allows them to identify who is speaking to whom, which makes it possible to mine character-to-character sentiment by summing the valence values of words that appear in the continuous direct speech and are found in the lexicon[92] of affective norms. The extension[93] of the previous research from the same authors introduces the concept of a »sentiment network«, a dynamic social network of characters. Changing polarities between characters are modeled as edge weights in the network. Motivated by the desire to explain such networks in terms of a general sociological model, Nalisnick and Baird test whether Shakespeare’s plays obey the Structural Balance Theory by Marvel et al.[94] that postulates that a friend of a friend is also your friend. Using the procedure proposed by Marvel et al. on their Shakespearean sentiment networks, Nalisnick and Baird test whether they can predict how a play’s characters will split into factions using only information about the state of the sentiment network after Act II. The results of their analysis are varied and do not provide adequate support for the Structural Balance Theory as a benchmark for network analysis in Shakespeare’s plays. One reason for that, as the authors state, is inadequacy of their shallow sentiment analysis methods that cannot detect such elements of speech as irony and deceit that play a pivotal role in many literary works.

4.4.2 Character analysis and character relationships

[62]Elsner[95] aims at answering the question of how to represent a plot structure for summarization and generation tools. To that end, Elsner presents a kernel for comparing novelistic plots at the level of character interactions and their relationships. Using sentiment as one of the properties of a character, Elsner demonstrates that the kernel approach leads to meaningful plot representation that can be used for a higher-level processing.

[63]Kim and Klinger[96] aim at understanding the causes of emotions experienced by literary characters. To that end, they contribute the REMAN corpus[97] of literary texts with annotations of emotions, experiencers, causes and targets of the emotions. The goal of the project is to enable the automatic extraction of emotions and causes of emotions experienced by the characters. The authors suggest that the results of coarse-grained emotion classification in literary text are not readily interpretable as they do not tell much about who the experiencer of the emotion is. Indeed, if a text mentions two characters, one of whom is angry and another one who is scared because of that, text classification models will only tell us that the text is about anger and fear. Hence, a finer-grained approach towards character relationship extraction is warranted. Kim and Klinger conduct experiments on the annotated dataset showing that the fine-grained approach to emotion prediction with long short-term memory networks outperforms bag-of-words models. At the same time, the results of their experiments suggest that joint prediction of emotions and experiencers can be more beneficial than studying these categories separately.

[64]A tool presented by Jhavar and Mirza[98] provides a similar functionality: given an input of two character names from the Harry Potter series, the EMoFiel[99] tool identifies the emotion flow between a given directed pair of story characters. These emotions are identified using categorical[100] and continuous[101] emotion models.

[65]Egloff et al.[102] present an ongoing work on the Ontology of Literary Characters (OLC) that allows us to capture and infer characters’ psychological traits from their linguistic descriptions. The OLC incorporates the Ontology of Emotion[103] that is based on both Plutchik’s and Hourglass’s[104] models of emotions. The ontology encodes 32 emotion concepts. Based on their natural language description, characters are attributed to a psychological profile along the classes of Openness to experience, Conscientiousness, Extraversion, Agreeableness, and Neuroticism. The ontology links each of these profiles to one or more archetypal categories of hero, anti-hero, and villain. Egloff et al. argue that, by using the semantic connections of the OLC, it is possible to infer the characters’ psychological profiles and the role they play in the plot.

[66]Kim and Klinger[105] propose the task of emotion relationship classification between fictional characters. They argue that joining character network analysis with sentiment and emotion analysis may contribute to a computational understanding of narrative structures, as characters are at the center of any plot development. Building a corpus of 19 fan fiction short stories and annotating it with emotions, Kim and Klinger propose several models to classify emotion relations of characters. They show that a deep learning architecture with character position indicators is the best for the task of predicting both directed and undirected emotion relations in the associated social network graph. As an extension to this study, Kim and Klinger[106] explore how emotions are expressed between characters in the same corpus via various non-verbal communication channels.[107] They find that facial expressions are predominantly associated with joy while gestures and body postures are more likely to occur with trust.

[67]Finally, a small body of work focuses on mathematical modeling of character relationships. Rinaldi et al.[108] contribute a model that describes the love story between the Beauty and the Beast through ordinary differential equations. Zhuravlev et al.[109] introduce a distance function to model the relationship between the protagonist and other characters in two masochistic short novels by Ivan Turgenev and Sacher-Masoch. Borrowing some instruments from the literary criticism and using ordinary differential equations, Zhuravlev et al. are able to reproduce the temporal and spatial dynamics of the love plot in the two novellas more precisely than it had been done in previous research. Jafari et al.[110] present a dynamic model describing the development of character relationships based on differential equations. The proposed model is enriched with complex variables that can represent complex emotions such as coexisting love and hate.

4.5.1 Emotion flow analysis and visualization

[69]A set of authors aimed to visualize the change of emotion content through texts or across time. One of the earliest works in this direction is a paper by Anderson and McMaster[111] that starts from the premise that reading enjoyment stems from the affective tones of a text. These affective tones create a conflict that can rise to a climax through a series of crises, which is necessary for a work of fiction to be attractive to the reader. Using a list of 1,000 of the most common English words annotated with valence, arousal, and dominance ratings,[112] they calculate the conflict score by taking the mean of the ratings for each word in a text passage. The more negative the score is, the higher the conflict is, and vice versa. Additionally, they plot conflict scores for each consecutive 100 words of a test story and provide qualitative analysis of the peaks. They argue that a reader who has access to the text would be able to find correlation between events in the story and peaks on the graph. However, the authors still stress that such interpretation remains dependent upon the judgement of the reader. Further, other contributions by the authors are based on the same premises.[113]

[70]Alm and Sproat[114] present the results of the emotion annotation task of 22 tales by the Grimm brothers and evaluate patterns of emotional story development. They split emotions into positive and negative categories and divide each story into five parts from which aggregate frequency counts of combined emotion categories are computed. The resulting numbers are plotted on a graph that shows a wave-shaped pattern. From this graph, Alm and Sproat argue, one can see that the first part of the fairy tales is the least emotional, which is probably due to scene setting, while the last part shows an increase in positive emotions, which may signify the happy ending.

[71]Two other studies by Mohammad[115] focus on differences in emotion word density as well as emotional trajectories between books of different genres. Emotion word density is defined as a number of times a reader will encounter an emotion word on reading every X words. In addition, each text is assigned several emotion scores for each emotion that are calculated as a ratio of words associated with one emotion to the total number of emotion words occurring in a text. Both metrics use the NRC Affective Lexicon to find occurrences of emotion words. They find that fairy tales have significantly higher anticipation, disgust, joy and surprise word densities, but lower trust word densities when compared to novels.

[72]A work by Klinger et al.[116] is a case 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 as well as to provide a character-oriented emotion analysis that would reveal specific character traits in both texts. To that end, Klinger et al. develop German dictionaries of words associated with Ekman’s fundamental emotions plus contempt and apply them to both texts in question to automatically detect emotion words. The results of their analysis for Das Schloss show a striking increase of surprise towards the end and a peak of fear shortly after start of chapter 3. In the case of Amerika, the analysis shows that there is a decrease in enjoyment after a peak in chapter 4.

[73]A similar study by Schmidt and Burghardt[117] also works on German text – but focuses on the mostly neglected domain of theater plays, more concretely the plays by Lessing. They perform an annotation study and subsequently analyze different established emotion lexicons to recover the emotion automatically. The configuration of the best performing system shows the highest accuracy of 0.7, while a majority baseline obtains 0.695.

[74]Yet another work that tracks the flow of emotions in a collection of texts is presented by Kim et al.[118] The authors hypothesize that literary genres can be linked to the development of emotions over the course of text. To test this, they collect more than 2,000 books from five genres (adventure, science fiction, mystery, humor and romance) from Project Gutenberg and identify prototypical emotion shapes for each genre. Each novel in the corpus is split into five consecutive equally-sized segments (following the five-act theory of dramatic acts).[119] All five genres show close correspondence with regard to sadness, anger, fear and disgust, i.e., a consistent increase of these emotions from Act 1 to Act 5, which may correspond to an entertaining narrative. Mystery and science fiction books show increase in anger towards the end, and joy shows an inverse decreasing pattern from Act 1 to Act 2, with the exception of humor.

[75]The work by Kakkonen and Galic Kakkonen[120] aims at supporting the literary analysis of Gothic texts at the sentiment level. The authors introduce a system called SentiProfiler that generates visual representations of affective content in such texts and outlines similarities and differences between them, however, without considering the temporal dimension. The SentiProfiler uses WordNet-Affect to derive a list of emotion-bearing words that will be used for analysis. The resulting sentiment profiles for the books are used to visualize the presence of sentiment in a particular document and to compare two different texts.

4.5.2 Miscellaneous

[76]In this section, we review studies that are different in goals and research questions from the papers presented in previous sections and do not constitute a category on their own.

[77]Koolen[121] claims that there is a bias among readers that put works by female authors on par with »women’s books«, which, as stated by the author, tend to be perceived as of lower literary quality. She investigates how much »women’s books« (here, romantic novels written by women) differ from novels perceived as literary (female and male-authored literary fiction). The corpus used in the study is a collection of European and North-American novels translated into Dutch. Koolen uses a Dutch version of the Linguistic Inquiry and Word Count,[122] a dictionary that contains content and sentiment-related categories of words to count the number of words from different categories in each type of fiction. Her analysis shows that romantic novels contain more positive emotions and words pertaining to friendship than in literary fiction. However, female-authored literary novels and male-authored ones do not significantly differ on any category.

[78]Kraicer and Piper[123] explore the women’s place within contemporary fiction starting from the premise that there is a near ubiquitous underrepresentation and decentralization of women. As a part of their analysis, Kraicer and Piper use sentiment scores to look at social balance and »antagonism«, i.e., how different gender pairings influence positive and negative language surrounding the co-occurrence of characters (using the sentiment dictionary presented by Liu[124] to calculate a sentiment score for a character pair). Having analyzed a set of 26,450 characters from 1,333 novels published between 2001 and 2015, the authors find that sentiment scores give little indication that the character’s gender has an effect on the state of social balance.

[79]Morin and Acerbi[125] focus on larger-scale data spanning a hundred thousand of books. The goal of their study is to understand how emotionality of written texts changed throughout the centuries. Having collected 307,527 books written between 1900 and 2000 from the Google Books corpus[126] they collect, for each year, the total number of case-insensitive occurrences of emotion terms that are found under positive and negative taxonomies of LIWC dictionary.[127] The main findings of their research show that emotionality (both positive and negative emotions) declines with time, and this decline is driven by the decrease in usage of positive vocabulary. Morin and Acerbi remind us that the Romantic period was dominated by emotionality in writing, which could be the effect of a group of writers who wrote above the mean. If one assumes that each new writer tends to copy the emotional style of their predecessors, then writers at one point of time are disproportionally influenced by this group of above-the-mean writers. However, this trend does not last forever and, sooner or later, the trend reverts to the mean, as each writer reverts to a normal level of emotionality.

[80]An earlier work[128] written in collaboration with Acerbi provides a somewhat different approach and interpretation of the problem of the decline in positive vocabulary in English books of the twentieth century. Using the same dataset and lexical resources (plus WordNet-Affect) Bentley et al. find a strong correlation between expressed negative emotions and the U.S. economic misery index, which is especially strong for the books written during and after the World War I, the Great Depression, and the energy crisis in the 1970s. However, in the present study,[129] the authors argue that the extent to which positive emotionality correlates with subjective well-being is a debatable issue. Morin and Acerbi provide more possible reasons for this effect as well as detailed statistical analysis of the data, so we refer the reader to the original paper for more information.

Tab. 1: Summary of characteristics of methods used in the papers reviewed in this survey. Download as PDF. [Kim / Klinger 2021]