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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| Published in | Multimedia tools and applications Vol. 78; no. 11; pp. 15169 - 15211 |
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| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Springer US
01.06.2019
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Li, Yanchao Jelodar, Hamed Yuan, Chi Zhao, Liang Wang, Yongli Feng, Xia Jiang, Xiahui |
| Author_xml | – sequence: 1 givenname: Hamed surname: Jelodar fullname: Jelodar, Hamed organization: School of Computer Science and Technology, Nanjing University of Science and Technology – sequence: 2 givenname: Yongli surname: Wang fullname: Wang, Yongli email: YongliWang@njust.edu.cn organization: School of Computer Science and Technology, Nanjing University of Science and Technology, China Electronics Technology Cyber Security Co., Ltd – sequence: 3 givenname: Chi surname: Yuan fullname: Yuan, Chi organization: School of Computer Science and Technology, Nanjing University of Science and Technology – sequence: 4 givenname: Xia surname: Feng fullname: Feng, Xia organization: School of Computer Science and Technology, Nanjing University of Science and Technology – sequence: 5 givenname: Xiahui surname: Jiang fullname: Jiang, Xiahui organization: School of Computer Science and Technology, Nanjing University of Science and Technology – sequence: 6 givenname: Yanchao surname: Li fullname: Li, Yanchao organization: School of Computer Science and Technology, Nanjing University of Science and Technology – sequence: 7 givenname: Liang surname: Zhao fullname: Zhao, Liang organization: School of Computer Science and Technology, Nanjing University of Science and Technology |
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| Title | Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey |
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