Kolmogorova A.V., Sun Qiuhua. Texts of Different Emotional Classes and Their Topic Modeling
DOI: https://doi.org/10.15688/jvolsu2.2024.5.5
Anastasia V. Kolmogorova
Doctor of Sciences (Philology), Professor, Head of the Laboratory of Language Convergence, HSE University
kanala Griboyedova Emb., 119–121, 190068 Saint Petersburg, Russia
This email address is being protected from spambots. You need JavaScript enabled to view it.
https://orcid.org/0000-0002-6425-2050
Qiuhua Sun
Doctor of Sciences (Philology), Professor, Head of the Department for International Cooperation, Heilongjiang University
Prosp. Xuefu, 74, Harbin, China
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https://orcid.org/0000-0002-1959-7180
Abstract. The article is devoted to studying verbalization specifics of various emotional states in the texts in the Russian language 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 the Russian language, 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 H. Lovheim'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 nonrandom 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. 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.
Key words: emotions, denotative situation, topic modeling, social network texts, Russian language.
Citation. Kolmogorova A.V., Sun Qiuhua. Texts of Different Emotional Classes and Their Topic Modeling. Vestnik Volgogradskogo gosudarstvennogo universiteta. Seriya 2. Yazykoznanie [Science Journal of Volgograd State University. Linguistics], 2024, vol. 23, no. 5, pp. 60-71. DOI: https://doi.org/10.15688/jvolsu2.2024.5.5
Texts of Different Emotional Classes and Their Topic Modeling by Kolmogorova A.V., Sun Qiuhua is licensed under CC BY 4.0
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