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Emotional Sentence Annotation Helps Predict Fiction Genre.

Samothrakis S, Fasli M - PLoS ONE (2015)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Institute for Analytics and Data Science, University of Essex, Wivenhoe Park, Colchester CO4 3SQ, United Kingdom.

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.

No MeSH data available.


Emotional content for Murder at Bridge, by Anne Austin, of class Mystery.
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pone.0141922.g001: Emotional content for Murder at Bridge, by Anne Austin, of class Mystery.

Mentions: We then take the average of the signal for each n sentences, where ∣tss∣/c, where c = 50 is a constant. This in effect creates a smoothed version of the signals with 50 timesteps, no matter how big the original signal length (i.e, the size of the work of fiction) was. This however meant that some works that had less than 50 sentences in total had to be removed, which brought down the total sample size to 3377. Sample signals of this type for all emotions can be seen for two novels in Figs 1 and 2. Notice the continuous fluctuation of signal strength. The smoothing/averaging process described has the explicit goal of turning a very noisy signal to a version that can be fed into a classifier and that minor differences are removed. The noise comes from multiple sources (e.g., errors in the emotional content analysis) but the size of the overall text allows for the total feeling to be captured.


Emotional Sentence Annotation Helps Predict Fiction Genre.

Samothrakis S, Fasli M - PLoS ONE (2015)

Emotional content for Murder at Bridge, by Anne Austin, of class Mystery.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4629906&req=5

pone.0141922.g001: Emotional content for Murder at Bridge, by Anne Austin, of class Mystery.
Mentions: We then take the average of the signal for each n sentences, where ∣tss∣/c, where c = 50 is a constant. This in effect creates a smoothed version of the signals with 50 timesteps, no matter how big the original signal length (i.e, the size of the work of fiction) was. This however meant that some works that had less than 50 sentences in total had to be removed, which brought down the total sample size to 3377. Sample signals of this type for all emotions can be seen for two novels in Figs 1 and 2. Notice the continuous fluctuation of signal strength. The smoothing/averaging process described has the explicit goal of turning a very noisy signal to a version that can be fed into a classifier and that minor differences are removed. The noise comes from multiple sources (e.g., errors in the emotional content analysis) but the size of the overall text allows for the total feeling to be captured.

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Institute for Analytics and Data Science, University of Essex, Wivenhoe Park, Colchester CO4 3SQ, United Kingdom.

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.

No MeSH data available.