Continuous sentiment scores for literary and multilingual contexts

Laurits Lyngbaek, Pascale Feldkamp, Yuri Bizzoni, Kristoffer Nielbo, Kenneth Enevoldsen

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 senti-
ment 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 histor-
ical 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.