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. There is plenty of evidence that emotion analysis has many valuable applications. In this study a blog emotion corpus is constructed for Chinese emotional expression analysis. This corpus contains manual annotation of eight emotional categories (expect, joy, love, surprise, anxiety, sorrow, angry and hate), emotion intensity, emotion holder target, emotional word phrase, degree word, negative word, conjunction, rhetoric, punctuation and other linguistic expressions that indicate emotion. Annotation agreement analyses for emotion classes and emotional words and phrases are described. Then, using this corpus, we explore emotion expressions in Chinese and present the analyses on them. Though data-driven in nature, emotion analysis based on latent semantic analysis still relies on some measure of expert knowledge in order to isolate the emotional keywords or keysets necessary to the construction of affective categories. This makes it vulnerable to any discrepancy between the ensuing taxonomy of affective states and the underlying domain of discourse. This paper proposes a more general strategy which leverages two distincts semantic levels, one that encapsulates the foundations of the domain considered, and one that specifically accounts for the overall affective fabric of the language. Exposing the emergent relationship between these two levels advantageously informs the emotion classification process. Empirical evidence suggests that this is a promising solution for automatic emotion detection in text. 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.