Text Classification by Genre Based on Rhythm Features
The article is devoted to the analysis of the rhythm of texts of different genres: fiction novels, advertisements, scientific articles, reviews, tweets, and political articles. The authors identified lexico-grammatical figures in the texts: anaphora, epiphora, diacope, aposiopesis, etc., that are ma...
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| Format: | Article |
| Language: | English |
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Yaroslavl State University
2021-10-01
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| Series: | Моделирование и анализ информационных систем |
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| Online Access: | https://www.mais-journal.ru/jour/article/view/1528 |
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| author | Ksenia Vladimirovna Lagutina Nadezhda Stanislavovna Lagutina Elena Igorevna Boychuk |
| author_facet | Ksenia Vladimirovna Lagutina Nadezhda Stanislavovna Lagutina Elena Igorevna Boychuk |
| author_sort | Ksenia Vladimirovna Lagutina |
| collection | DOAJ |
| description | The article is devoted to the analysis of the rhythm of texts of different genres: fiction novels, advertisements, scientific articles, reviews, tweets, and political articles. The authors identified lexico-grammatical figures in the texts: anaphora, epiphora, diacope, aposiopesis, etc., that are markers of the text rhythm. On their basis, statistical features were calculated that describe quantitatively and structurally these rhythm features.The resulting text model was visualized for statistical analysis using boxplots and heat maps that showed differences in the rhythm of texts of different genres. The boxplots showed that almost all genres differ from each other in terms of the overall density of rhythm features. Heatmaps showed different rhythm patterns across genres. Further, the rhythm features were successfully used to classify texts into six genres. The classification was carried out in two ways: a binary classification for each genre in order to separate a particular genre from the rest genres, and a multi-class classification of the text corpus into six genres at once. Two text corpora in English and Russian were used for the experiments. Each corpus contains 100 fiction novels, scientific articles, advertisements and tweets, 50 reviews and political articles, i.e. a total of 500 texts. The high quality of the classification with neural networks showed that rhythm features are a good marker for most genres, especially fiction. The experiments were carried out using the ProseRhythmDetector software tool for Russian and English languages. Text corpora contains 300 texts for each language. |
| format | Article |
| id | doaj-art-2dd6ca4034d144288910b3860f3e0981 |
| institution | DOAJ |
| issn | 1818-1015 2313-5417 |
| language | English |
| publishDate | 2021-10-01 |
| publisher | Yaroslavl State University |
| record_format | Article |
| series | Моделирование и анализ информационных систем |
| spelling | doaj-art-2dd6ca4034d144288910b3860f3e09812025-08-20T03:22:03ZengYaroslavl State UniversityМоделирование и анализ информационных систем1818-10152313-54172021-10-0128328029110.18255/1818-1015-2021-3-280-2911163Text Classification by Genre Based on Rhythm FeaturesKsenia Vladimirovna Lagutina0Nadezhda Stanislavovna Lagutina1Elena Igorevna Boychuk2P.G. Demidov Yaroslavl State UniversityP.G. Demidov Yaroslavl State UniversityYaroslavl State Pedagogical University named after K.D. UshinskyThe article is devoted to the analysis of the rhythm of texts of different genres: fiction novels, advertisements, scientific articles, reviews, tweets, and political articles. The authors identified lexico-grammatical figures in the texts: anaphora, epiphora, diacope, aposiopesis, etc., that are markers of the text rhythm. On their basis, statistical features were calculated that describe quantitatively and structurally these rhythm features.The resulting text model was visualized for statistical analysis using boxplots and heat maps that showed differences in the rhythm of texts of different genres. The boxplots showed that almost all genres differ from each other in terms of the overall density of rhythm features. Heatmaps showed different rhythm patterns across genres. Further, the rhythm features were successfully used to classify texts into six genres. The classification was carried out in two ways: a binary classification for each genre in order to separate a particular genre from the rest genres, and a multi-class classification of the text corpus into six genres at once. Two text corpora in English and Russian were used for the experiments. Each corpus contains 100 fiction novels, scientific articles, advertisements and tweets, 50 reviews and political articles, i.e. a total of 500 texts. The high quality of the classification with neural networks showed that rhythm features are a good marker for most genres, especially fiction. The experiments were carried out using the ProseRhythmDetector software tool for Russian and English languages. Text corpora contains 300 texts for each language.https://www.mais-journal.ru/jour/article/view/1528stylometrynatural language processingrhythm featuresgenrestext classification |
| spellingShingle | Ksenia Vladimirovna Lagutina Nadezhda Stanislavovna Lagutina Elena Igorevna Boychuk Text Classification by Genre Based on Rhythm Features Моделирование и анализ информационных систем stylometry natural language processing rhythm features genres text classification |
| title | Text Classification by Genre Based on Rhythm Features |
| title_full | Text Classification by Genre Based on Rhythm Features |
| title_fullStr | Text Classification by Genre Based on Rhythm Features |
| title_full_unstemmed | Text Classification by Genre Based on Rhythm Features |
| title_short | Text Classification by Genre Based on Rhythm Features |
| title_sort | text classification by genre based on rhythm features |
| topic | stylometry natural language processing rhythm features genres text classification |
| url | https://www.mais-journal.ru/jour/article/view/1528 |
| work_keys_str_mv | AT kseniavladimirovnalagutina textclassificationbygenrebasedonrhythmfeatures AT nadezhdastanislavovnalagutina textclassificationbygenrebasedonrhythmfeatures AT elenaigorevnaboychuk textclassificationbygenrebasedonrhythmfeatures |