Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey

Topic modeling is one of the most powerful techniques in text mining for data mining, latent data discovery, and finding relationships among data and text documents. Researchers have published many articles in the field of topic modeling and applied in various fields such as software engineering, po...

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Veröffentlicht in:Multimedia tools and applications Jg. 78; H. 11; S. 15169 - 15211
Hauptverfasser: Jelodar, Hamed, Wang, Yongli, Yuan, Chi, Feng, Xia, Jiang, Xiahui, Li, Yanchao, Zhao, Liang
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.06.2019
Springer Nature B.V
Schlagworte:
Computer Communication Networks
Computer Science
Data mining
Data Structures and Information Theory
Dirichlet problem
Modelling
Multimedia Information Systems
Researchers
Software engineering
Special Purpose and Application-Based Systems
Topic modeling
Semantic web
Latent Dirichlet allocation
Gibbs sampling
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ISSN:1380-7501, 1573-7721
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Zusammenfassung:Topic modeling is one of the most powerful techniques in text mining for data mining, latent data discovery, and finding relationships among data and text documents. Researchers have published many articles in the field of topic modeling and applied in various fields such as software engineering, political science, medical and linguistic science, etc. There are various methods for topic modelling; Latent Dirichlet Allocation (LDA) is one of the most popular in this field. Researchers have proposed various models based on the LDA in topic modeling. According to previous work, this paper will be very useful and valuable for introducing LDA approaches in topic modeling. In this paper, we investigated highly scholarly articles (between 2003 to 2016) related to topic modeling based on LDA to discover the research development, current trends and intellectual structure of topic modeling. In addition, we summarize challenges and introduce famous tools and datasets in topic modeling based on LDA.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-018-6894-4