Centrality of emotion for the stories told by humans is underpinned by numerous studies in literature and psychology. The research in automatic storytelling has recently turned towards emotional storytelling, in which characters' emotions play an important role in the plot development. However, these studies mainly use emotion to generate propositional statements in the form "A feels affection towards B" or "A confronts B". At the same time, emotional behavior does not boil down to such propositional descriptions, as humans display complex and highly variable patterns in communicating their emotions, both verbally and non-verbally. In this paper, we analyze how emotions are expressed non-verbally in a corpus of fan fiction short stories. Our analysis shows that stories written by humans convey character emotions along various non-verbal channels. We find that some non-verbal channels, such as facial expressions and voice characteristics of the characters, are more strongly associated with joy, while gestures and body postures are more likely to occur with trust. Based on our analysis, we argue that automatic storytelling systems should take variability of emotion into account when generating descriptions of characters' emotions.
More recent studies in the field of digital humanities approach emotion analysis from various angles and for a wide range of goals. Some studies use emotions as feature input for genre classification @cite_25 @cite_3 @cite_17 @cite_21 , story clustering @cite_14 , mapping emotions to geographical locations in literature @cite_26 , and construction of social networks of characters @cite_22 @cite_19 . Other studies use emotion analysis as a starting point for stylometry @cite_2 , inferring psychological characters' traits @cite_1 , and analysis of the causes of emotions in literature @cite_9 @cite_6 .
@cite_14 @cite_26 @cite_22 @cite_9 @cite_21 @cite_1 @cite_3 @cite_6 @cite_19 @cite_2 @cite_25 @cite_17
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Advances in computing power, natural language processing, and digitization of text now make it possible to study a culture’s evolution through its texts using a ‘big data’ lens. Our ability to communicate relies in part upon a shared emotional experience, with stories often following distinct emotional trajectories and forming patterns that are meaningful to us. Here, by classifying the emotional arcs for a filtered subset of 1,327 stories from Project Gutenberg’s fiction collection, we find a set of six core emotional arcs which form the essential building blocks of complex emotional trajectories. We strengthen our findings by separately applying matrix decomposition, supervised learning, and unsupervised learning. For each of these six core emotional arcs, we examine the closest characteristic stories in publication today and find that particular emotional arcs enjoy greater success, as measured by downloads. Automatic methods for analyzing sentiment and its movement through a play's social network are investigated. From structured dialogue we can algorithmically determine who is speaking and guess at who is listening or being directly addressed. Knowing who is speaking to whom allows the flow of sentiment to be tracked between characters and, within plays with clear time-lines, permits tracking the development of emotional relationships. We hypothesize that changing polarities between characters can be modeled as edge weights in a dynamic social network- a "sentiment network"-which can be used to distinguish a document's genre (tragedies versus comedies), detect a given character's enemies and allies, and model the overall emotional development of a play. Experiments on Shakespeare's plays are presented along with discussion of improvements and further applications. We present EMOFIEL, a system that identifies characters and scenes in a story from a fictional narrative summary, generates appropriate scene descriptions, identifies the emotion flow between a given directed pair of story characters in each interaction, and organizes them along the story timeline. These emotions are identified using two emotion modelling approaches: categorical and dimensional emotion models. The generated plots show that in a particular scene, two characters can share multiple emotions together with different intensity. Furthermore, the directionality of the emotion can be captured as well, depending on which character is more dominant in each interaction. EMOFIEL provides a web-based GUI that allows users to query the annotated stories to explore the emotion mapping of a given character pair throughout a given story, and to explore scenes for which a certain emotion peaks. In this paper, we show how sentiment analysis can be used in tandem with effective visualizations to quantify and track emotions in mail and books. We study a number of specific datasets and show, among other things, how collections of texts can be organized for affect-based search and how books portray different entities through co-occurring emotion words. Analysis of the Enron Email Corpus reveals that there are marked differences across genders in how they use emotion words in work-place email. Finally, we show that fairy tales have more extreme emotion densities than novels. Fiction, a prime form of entertainment, has evolved into multiple genres which one can broadly attribute to different forms of stories. In this paper, we examine the hypothesis that works of fiction can be characterised by the emotions they portray. To investigate this hypothesis, we use the work of fictions in the Project Gutenberg and we attribute basic emotional content to each individual sentence using Ekman’s model. A time-smoothed version of the emotional content for each basic emotion is used to train extremely randomized trees. We show through 10-fold Cross-Validation that the emotional content of each work of fiction can help identify each genre with significantly higher probability than random. We also show that the most important differentiator between genre novels is fear. Text classification methods have been evaluated on topic classification tasks. This thesis extends the empirical evaluation to emotion classification tasks in the literary domain. This study selects two literary text classification problems---the eroticism classification in Dickinson's poems and the sentimentalism classification in early American novels---as two cases for this evaluation. Both problems focus on identifying certain kinds of emotion---a document property other than topic. This study chooses two popular text classification algorithms---naive Bayes and Support Vector Machines (SVM), and three feature engineering options---stemming, stopword removal and statistical feature selection (Odds Ratio and SVM)---as the subjects of evaluation. This study aims to examine the effects of the chosen classifiers and feature engineering options on the two emotion classification problems, and the interaction between the classifiers and the feature engineering options. This thesis seeks empirical answers to the following research questions: (1) is SVM a better classifier than naive Bayes regarding classification accuracy, new literary knowledge discovery and potential for example-based retrieval? (2) is SVM a better feature selection method than Odds Ratio regarding feature reduction rate and classification accuracy improvement? (3) does stop word removal affect the classification performance? (4) does stemming affect the performance of classifiers and feature selection methods? Some of our conclusions are consistent with what are obtained in topic classification, such as Odds Ratio does not improve SVM performance and stop word removal might harm classification. Some conclusions contradict previous results, such as SVM does not beat naive Bayes in both cases. Some findings are new to this area---SVM and naive Bayes select top features in different frequency ranges; stemming might harm feature selection methods. These experiment results provide new insights to the relation between classification methods, feature engineering options and non-topic document properties. They also provide guidance for classification method selection in literary text classification applications.