Emotional Sentence Annotation Helps Predict Fiction Genre

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...

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Bibliographic Details
Published in:	PloS one 2015-11, Vol.10 (11), p.e0141922-e0141922
Main Authors:	Samothrakis, Spyridon, Fasli, Maria
Format:	Article
Language:	eng
Subjects:	Analysis Annotations Decision making Emotions Humans Linguistics Motion Perception Motion Pictures - classification Novels Sentiment analysis
Online Access:	Get full text
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Summary:	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.
ISSN:	1932-6203 1932-6203