Sentiment Analysis of Literary Texts vs. Reader's Emotional Responses

Abstract

Sentiment analysis is a relevant task in natural language processing, which is often conducted on Internet texts to analyze reviews of products, services, posts, and comments on social media. Unlike most studies, our work focuses on literary texts in the Russian language written during the emotionally charged period of the early 20th century, when a change in the Russian political system and a fundamental shift in society's way of life occurred as a result of socio-economic upheavals such as wars and revolutions. It is assumed that literary texts from this period should also be rich in emotional vocabulary. The main goal of the research described in this article is to study the correlation between the results of sentiment analysis, performed on the material of Russian short stories using several automatic methods, and the average expert evaluation of the emotions that these same texts evoke in modern readers. The results of the study contribute to understanding the evaluative component of literary works vocabulary and can also be used to build recommendation systems aimed at selecting literary texts for readers.

Keywords

• nlp; sentiment analysis; fiction; russian short story; text perception; reader’s reception; assessment; emotional reaction; quantitative analysis