Emotional Sentence Annotation Helps Predict Fiction Genre

Spyridon Samothrakis, Maria Fasli, Zhaohong Deng
2015 PLoS ONE  
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
more » ... 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. We perform 10-fold cross-validation using all samples, as this was shown empirically to have an optimal bias-variance tradeoff [20] . We train extremely random forests [21] using scikitlearn's Python implementation [22] of the algorithm. We use 1500 trees. A scaled average version (between [0, 1]) of all the confusion matrices of the results can be seen in Fig 3 and the Emotional Sentence Annotation Helps Predict Fiction Genre PLOS ONE |
doi:10.1371/journal.pone.0141922 pmid:26524352 pmcid:PMC4629906 fatcat:ef6iawhc3bc4hehtgwptrcd3fi