{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/171104305","title":"Latent Dirichlet Allocation (LDA) and Topic modeling: models, applications, a survey","arxiv_id":"1711.04305","date":"2017-11-12","proceeding":null,"authors":["Hamed Jelodar","Yongli Wang","Chi Yuan","Xia Feng","Xiahui Jiang","Yanchao Li","Liang Zhao"],"abstract":"Topic modeling is one of the most powerful techniques in text mining for data\nmining, latent data discovery, and finding relationships among data, text\ndocuments. Researchers have published many articles in the field of topic\nmodeling and applied in various fields such as software engineering, political\nscience, medical and linguistic science, etc. There are various methods for\ntopic modeling, which Latent Dirichlet allocation (LDA) is one of the most\npopular methods in this field. Researchers have proposed various models based\non the LDA in topic modeling. According to previous work, this paper can be\nvery useful and valuable for introducing LDA approaches in topic modeling. In\nthis paper, we investigated scholarly articles highly (between 2003 to 2016)\nrelated to Topic Modeling based on LDA to discover the research development,\ncurrent trends and intellectual structure of topic modeling. Also, we summarize\nchallenges and introduce famous tools and datasets in topic modeling based on\nLDA.","url_abs":"http://arxiv.org/abs/1711.04305v2","url_pdf":"http://arxiv.org/pdf/1711.04305v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"171104305","repo_url":"https://github.com/rajesh-gupta-14/LookAlike_Model_B2B_Customers_NLP","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"articles","task_name":"Articles"}],"methods":[{"method_slug":"lda","method_name":"LDA"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}