Continuous sentiment scores for literary and multilingual contexts

TL;DR

This paper presents a novel continuous sentiment scoring method tailored for literary and multilingual texts, overcoming limitations of traditional tools and transformer models by capturing nuanced sentiment expressions more effectively.

Contribution

It introduces a concept vector projection-based sentiment scoring approach trained on multilingual literary data, enhancing fine-grained sentiment analysis across languages and genres.

Findings

Outperforms existing sentiment tools on English and Danish texts

Produces sentiment scores closely aligned with human ratings

Enables more accurate sentiment arc modeling in literature

Abstract

Sentiment Analysis is widely used to quantify sentiment in text, but its application to literary texts poses unique challenges due to figurative language, stylistic ambiguity, as well as sentiment evocation strategies. Traditional dictionary-based tools often underperform, especially for low-resource languages, and transformer models, while promising, typically output coarse categorical labels that limit fine-grained analysis. We introduce a novel continuous sentiment scoring method based on concept vector projection, trained on multilingual literary data, which more effectively captures nuanced sentiment expressions across genres, languages, and historical periods. Our approach outperforms existing tools on English and Danish texts, producing sentiment scores whose distribution closely matches human ratings, enabling more accurate analysis and sentiment arc modeling in literature.