TEXTS OF DIFFERENT EMOTIONAL CLASSES AND THEIR TOPIC MODELING

Вестник Волгоградского государственного университета. Серия 2: Языкознание. 2024. Vol. 23. No. 5. P. 60–71.

Kolmogorova A., Qiuhua S.

The article is devoted to studying verbalization specifics of various emotional states in the texts in Russian with the purpose to confirm or refute the hypothesis that texts of different emotional classes reflect the denotative situation not identically, which is reflected in thematic specifics and lexical content. The research material consisted of eight corpus texts in Russian, which were extracted from the public pages of the social network VKontakte. The texts were selected according to emotional hashtags that corresponded to eight basic emotions, according to the H. Lövheim’s model: anger, surprise, shame, enjoyment, disgust, distress, excitement, fear. The correspondence of emotion and hashtag was established in a preliminary psycholinguistic experiment. While analyzing the text collection, we used the method of computer thematic modeling to identify statistically non–random groups of words (topics). We applied the BERTopic neural network model to the collected data. As a result of the analysis, it was found that texts of 8 emotional classes contain an uneven number of topics, despite the fact that their number does not correlate directly with the amount
of data: with a relatively small amount of data, there may be many topics, but in a voluminous corpus – few. For example, there are only two topics in the subcorpus of "inspired" texts (184,074 tokens), and nine in the subcorpus of "disgusting" texts (45,868 tokens). In addition, the sets of words (tokens) that make up each non-random group (topic) differ in each subcorpora, reflecting the specifics of the denotative situation, which is formed under the influence of the emotional state of the speaker. The idea of diverse thematic "granularity" of texts of different emotional classes is theoretically justified.

Keywords: русский язык, эмоции, Russian language, emotions, topic modeling, тематическое моделирование, denotative situation, social network texts, денотативная ситуация, тексты в социальных сетях

Publication based on the results of: Text as Big Data: methods and models for big text data analysis (2024)