Emotional Complexity as a Measure for Literary Reception

Anonymous

Abstract:

We introduce `EmotionArcs', a dataset comprising emotional arcs from over 9,000 English novels, assembled to understand the dynamics of emotions represented in text and how these emotions may influence a novel's reception and perceived quality. Through the paper, we discuss the challenges of emotion annotation, suggesting improvements based on theory and case studies to redefine how emotions are modeled in literary narratives. Finally, we use information-theoretic measures to analyze the impact of emotions on literary quality.
We find that emotional entropy, as well as the skewness and steepness of emotion arcs correlate with two proxies of literary reception. Our findings may offer insights into how quality assessments relate to emotional complexity and could help with the study of affect in literary novels.

Paper Type: long

Research Area: Sentiment Analysis, Stylistic Analysis, and Argument Mining

Contribution Types: NLP engineering experiment, Data resources, Data analysis

Languages Studied: English

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