{"title":"基于LDA、STM和NMF的俄语短文定性研究主题模型比较","authors":"M. Kirina","doi":"10.25205/1818-7935-2022-20-2-93-109","DOIUrl":null,"url":null,"abstract":"The paper describes the results of topic modelling of short prose fiction based on three methods, namely Latent Dirichlet Allocation (LDA), the Structural Topic Model (STM), and the Non-Negative Matrix Factorization (NMF), combined with different text preprocessing options (all parts of speech vs. only nouns). The experimental design is tested on the basis of the Corpus of Russian Short Stories of 1900–1930s. The research made it possible to determine the specifics of the algorithms under consideration and to assess the effectiveness of their application for the qualitative analysis of fiction texts.","PeriodicalId":434662,"journal":{"name":"NSU Vestnik. Series: Linguistics and Intercultural Communication","volume":"32 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Comparison of Topic Models Based on LDA, STM and NMF for Qualitative Studies of Russian Short Prose\",\"authors\":\"M. Kirina\",\"doi\":\"10.25205/1818-7935-2022-20-2-93-109\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The paper describes the results of topic modelling of short prose fiction based on three methods, namely Latent Dirichlet Allocation (LDA), the Structural Topic Model (STM), and the Non-Negative Matrix Factorization (NMF), combined with different text preprocessing options (all parts of speech vs. only nouns). The experimental design is tested on the basis of the Corpus of Russian Short Stories of 1900–1930s. The research made it possible to determine the specifics of the algorithms under consideration and to assess the effectiveness of their application for the qualitative analysis of fiction texts.\",\"PeriodicalId\":434662,\"journal\":{\"name\":\"NSU Vestnik. Series: Linguistics and Intercultural Communication\",\"volume\":\"32 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-06-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"NSU Vestnik. Series: Linguistics and Intercultural Communication\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.25205/1818-7935-2022-20-2-93-109\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"NSU Vestnik. Series: Linguistics and Intercultural Communication","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.25205/1818-7935-2022-20-2-93-109","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Comparison of Topic Models Based on LDA, STM and NMF for Qualitative Studies of Russian Short Prose
The paper describes the results of topic modelling of short prose fiction based on three methods, namely Latent Dirichlet Allocation (LDA), the Structural Topic Model (STM), and the Non-Negative Matrix Factorization (NMF), combined with different text preprocessing options (all parts of speech vs. only nouns). The experimental design is tested on the basis of the Corpus of Russian Short Stories of 1900–1930s. The research made it possible to determine the specifics of the algorithms under consideration and to assess the effectiveness of their application for the qualitative analysis of fiction texts.
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