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Emotional Sentence Annotation Helps Predict Fiction Genre

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  • Spyridon Samothrakis
  • Maria Fasli

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.

Suggested Citation

  • Spyridon Samothrakis & Maria Fasli, 2015. "Emotional Sentence Annotation Helps Predict Fiction Genre," PLOS ONE, Public Library of Science, vol. 10(11), pages 1-10, November.
    • Handle: RePEc:plo:pone00:0141922
    DOI: 10.1371/journal.pone.0141922
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    References listed on IDEAS

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    1. Alberto Acerbi & Vasileios Lampos & Philip Garnett & R Alexander Bentley, 2013. "The Expression of Emotions in 20th Century Books," PLOS ONE, Public Library of Science, vol. 8(3), pages 1-6, March.
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