- The paper introduces using sentiment analysis and visualization techniques, based on emotion word density and a crowdsourced lexicon, to track and quantify emotional components within literary texts.
- A key finding indicates that fairy tales exhibit wider variances and higher densities of certain emotions like anticipation and joy compared to novels, which show more trust-related words.
- The research offers practical implications for enhancing literary analysis and providing tools for digital humanities by enabling emotion-based exploration and organization of large text archives.
Sentiment Analysis in Literary Texts: Emotion Tracking in Novels and Fairy Tales
The extensive digitization of literary texts offers unprecedented opportunities for analysis beyond traditional keyword searches. In "From Once Upon a Time to Happily Ever After: Tracking Emotions in Novels and Fairy Tales," the paper explores how sentiment analysis combined with visualizations can track and quantify emotional components within and across literary works. This innovative approach aims to expand search capabilities, enabling users to explore texts based on emotions, rather than solely relying on keywords.
Methodology
The paper introduces the concept of emotion word density and employs a comprehensive word-emotion association lexicon in sentiment analysis. This lexicon, developed using crowdsourcing via Amazon's Mechanical Turk, encompasses emotion annotations for approximately 14,200 word types. Sentiment analysis is presented through visual metrics like emotion pie charts, timelines of emotional flows, and relative-salience word clouds, which depict the variance of emotion words within texts.
Findings
A central finding is the differentiation in emotion word densities between novels and fairy tales. Fairy tales exhibit wider variances in emotion word densities compared to novels. This implies a broader emotional expression within fairy tales, possibly attributed to their archetypal nature, morality-driven narratives, and fantastical elements. Notably, fairy tales demonstrate higher densities of anticipation, joy, disgust, and surprise, while novels contain more trust-related words.
Analyzing text collections such as the Brothers Grimm fairy tales, the research organizes texts based on emotion word densities to facilitate better search outcomes. For example, arranging tales in increasing order of negative word density can highlight the emotional variance across stories like "Cinderella" and "Godfather Death." Furthermore, by employing the Google Books N-gram Corpus, the paper assesses historical portrayals of entities via emotional word proximities, offering insights into socio-emotional dynamics over time.
Implications
The implications of this research extend into both theoretical and practical realms. Theoretically, it enhances understanding of emotional dynamics in literature, contributing to literary criticism and analysis by providing empirical data on emotional trends and variances. Practically, it offers tools for improved content organization, potentially beneficial for qualitative research, digital humanities, and user-focused literary exploration. The integration of emotion-based analysis could further aid in understanding socio-cultural influences on literary styles over time.
Future Prospects
The potential for advancement in sentiment analysis and emotion tracking algorithms could lead to more nuanced interpretations of emotional lexicons across various languages and cultures. Future developments could involve expanding emotion lexicons and improving emotion recognition systems to better handle irony and nuanced literary expressions.
In conclusion, this paper underscores the utility of sentiment analysis in unveiling emotional patterns in literary texts, setting a foundation for further research and application in computational linguistics and digital humanities. Through emotion tracking, the vast archives of digitized texts can be navigated with greater depth, offering enriched insights into the emotive power of literary works.