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This paper presents a comprehensive overview of topic modeling methods for short texts from a novel perspective. Firstly, it discusses short text probabilistic topic models and outlines the directions in which they can be improved. Secondly, it explores short text neural topic models, which can be categorized into three groups based on their underlying structures. In addition, this paper provides a detailed investigation of embedding methods in topic modeling. Moreover, various applications and corresponding works are surveyed, with a focus on short texts. The commonly used public corpora and evaluation indicators for topic modeling are also summarized. Finally, the advantages and disadvantages of short text topic modeling are discussed in detail, and future research directions are proposed.<\/jats:p>","DOI":"10.3233\/jifs-223834","type":"journal-article","created":{"date-parts":[[2023,5,19]],"date-time":"2023-05-19T12:24:00Z","timestamp":1684499040000},"page":"1971-1990","source":"Crossref","is-referenced-by-count":2,"title":["Topic modeling methods for short texts: A survey"],"prefix":"10.1177","volume":"45","author":[{"given":"Yuwei","family":"Fan","sequence":"first","affiliation":[{"name":"State Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing, China"}]},{"given":"Lei","family":"Shi","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing, China"},{"name":"Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin, China"}]},{"given":"Lu","family":"Yuan","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing, China"}]}],"member":"179","reference":[{"key":"10.3233\/JIFS-223834_ref1","first-page":"993","article-title":"Latent Dirichlet Allocation","volume":"3","author":"Blei","year":"2003","journal-title":"Journal of Machine Learning Research"},{"key":"10.3233\/JIFS-223834_ref2","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1023\/A:1007692713085","article-title":"Text Classification from Labeled and Unlabeled Documents using EM","volume":"39","author":"Nigam","year":"2000","journal-title":"Machine Learning"},{"key":"10.3233\/JIFS-223834_ref3","doi-asserted-by":"publisher","first-page":"975","DOI":"10.5120\/ijca2019919265","article-title":"A Detailed Survey on Topic Modeling for Document and Short Text Data","volume":"178","author":"Likhitha","year":"2019","journal-title":"International Journal of Computer Applications"},{"key":"10.3233\/JIFS-223834_ref4","doi-asserted-by":"publisher","first-page":"42","DOI":"10.3389\/frai.2020.00042","article-title":"Using Topic Modeling Methods for Short-Text Data: A Comparative Analysis","volume":"3","author":"Albalawi","year":"2020","journal-title":"Front. 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