TEXTS OF DIFFERENT EMOTIONAL CLASSES AND THEIR TOPIC MODELING
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...
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| Format: | Article |
| Language: | English |
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Volgograd State University
2024-11-01
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| Series: | Vestnik Volgogradskogo Gosudarstvennogo Universiteta. Seriâ 2. Âzykoznanie |
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| Online Access: | https://l.jvolsu.com/index.php/en/archive-en/928-science-journal-of-volsu-linguistics-2024-vol-23-no-5/artificial-intelligence-potential-in-natural-language-processing-and-machine-translation/2844-kolmogorova-a-v-sun-qiuhua-texts-of-different-emotional-classes-and-their-topic-modeling |
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| author | Anastasia V. Kolmogorova Qiuhua Sun |
| author_facet | Anastasia V. Kolmogorova Qiuhua Sun |
| author_sort | Anastasia V. Kolmogorova |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-aa6625795d5a492f805826d6034276a9 |
| institution | Kabale University |
| issn | 1998-9911 2409-1979 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Volgograd State University |
| record_format | Article |
| series | Vestnik Volgogradskogo Gosudarstvennogo Universiteta. Seriâ 2. Âzykoznanie |
| spelling | doaj-art-aa6625795d5a492f805826d6034276a92025-01-11T16:39:29ZengVolgograd State UniversityVestnik Volgogradskogo Gosudarstvennogo Universiteta. Seriâ 2. Âzykoznanie1998-99112409-19792024-11-01235607110.15688/jvolsu2.2024.5.5TEXTS OF DIFFERENT EMOTIONAL CLASSES AND THEIR TOPIC MODELINGAnastasia V. Kolmogorova0https://orcid.org/0000-0002-6425-2050Qiuhua Sun1https://orcid.org/0000-0002-1959-7180HSE University, Saint Petersburg, RussiaHeilongjiang University, Harbin, ChinaThe 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. https://l.jvolsu.com/index.php/en/archive-en/928-science-journal-of-volsu-linguistics-2024-vol-23-no-5/artificial-intelligence-potential-in-natural-language-processing-and-machine-translation/2844-kolmogorova-a-v-sun-qiuhua-texts-of-different-emotional-classes-and-their-topic-modelingemotionsdenotative situationtopic modelingsocial network textsrussian language |
| spellingShingle | Anastasia V. Kolmogorova Qiuhua Sun TEXTS OF DIFFERENT EMOTIONAL CLASSES AND THEIR TOPIC MODELING Vestnik Volgogradskogo Gosudarstvennogo Universiteta. Seriâ 2. Âzykoznanie emotions denotative situation topic modeling social network texts russian language |
| title | TEXTS OF DIFFERENT EMOTIONAL CLASSES AND THEIR TOPIC MODELING |
| title_full | TEXTS OF DIFFERENT EMOTIONAL CLASSES AND THEIR TOPIC MODELING |
| title_fullStr | TEXTS OF DIFFERENT EMOTIONAL CLASSES AND THEIR TOPIC MODELING |
| title_full_unstemmed | TEXTS OF DIFFERENT EMOTIONAL CLASSES AND THEIR TOPIC MODELING |
| title_short | TEXTS OF DIFFERENT EMOTIONAL CLASSES AND THEIR TOPIC MODELING |
| title_sort | texts of different emotional classes and their topic modeling |
| topic | emotions denotative situation topic modeling social network texts russian language |
| url | https://l.jvolsu.com/index.php/en/archive-en/928-science-journal-of-volsu-linguistics-2024-vol-23-no-5/artificial-intelligence-potential-in-natural-language-processing-and-machine-translation/2844-kolmogorova-a-v-sun-qiuhua-texts-of-different-emotional-classes-and-their-topic-modeling |
| work_keys_str_mv | AT anastasiavkolmogorova textsofdifferentemotionalclassesandtheirtopicmodeling AT qiuhuasun textsofdifferentemotionalclassesandtheirtopicmodeling |