A Survey on Sentiment and Emotion Analysis for Computational Literary Studies

TL;DR

This survey reviews computational approaches to emotion and sentiment analysis in literature, highlighting recent digital humanities methods for understanding narrative emotions and plot dynamics.

Contribution

It provides a comprehensive overview of computational emotion analysis in literary studies, emphasizing its emerging role within digital humanities.

Findings

Emotion analysis aids in understanding plot development.

Network analysis reveals emotional connections in texts.

Computational methods are increasingly used in literary emotion studies.

Abstract

Emotions are a crucial part of compelling narratives: literature tells us about people with goals, desires, passions, and intentions. Emotion analysis is part of the broader and larger field of sentiment analysis, and receives increasing attention in literary studies. In the past, the affective dimension of literature was mainly studied in the context of literary hermeneutics. However, with the emergence of the research field known as Digital Humanities (DH), some studies of emotions in a literary context have taken a computational turn. Given the fact that DH is still being formed as a field, this direction of research can be rendered relatively new. In this survey, we offer an overview of the existing body of research on emotion analysis as applied to literature. The research under review deals with a variety of topics including tracking dramatic changes of a plot development, network analysis of a literary text, and understanding the emotionality of texts, among other topics.