Samothrakis, S and Fasli, M (2015) 'Emotional sentence annotation helps predict fiction genre.' PLoS ONE, 10 (11). ISSN 1932-6203

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Abstract

Copyright © 2015 Samothrakis, Fasli. 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.

Item Type:	Article
Subjects:	P Language and Literature > PN Literature (General) Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User:	Jim Jamieson
Date Deposited:	05 Nov 2015 11:57
Last Modified:	17 Aug 2017 17:30
URI:	http://repository.essex.ac.uk/id/eprint/15399

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