title: Emotional Sentence Annotation Helps Predict Fiction Genre.
abstract: 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.
authors.affiliation: Institute for Analytics and Data Science, University of Essex, Wivenhoe Park, Colchester CO4 3SQ, United Kingdom.
authors.forename: Spyridon
authors.initials: S
authors.lastname: Samothrakis
authors.affiliation: Institute for Analytics and Data Science, University of Essex, Wivenhoe Park, Colchester CO4 3SQ, United Kingdom.
authors.forename: Maria
authors.initials: M
authors.lastname: Fasli
mesh_terms: D004644:Emotions; D006801:Humans; D009039:Motion Perception; D009040:Motion Pictures / Q000145:classification*