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.

Keywords

Latent Dirichlet allocationTopic modelComputer scienceData scienceField (mathematics)Artificial intelligenceInformation retrieval

Affiliated Institutions

Related Publications

A correlated topic model of Science

Topic models, such as latent Dirichlet allocation (LDA), can be useful tools for the statistical analysis of document collections and other discrete data. The LDA model assumes ...

2018 OPAL (Open@LaTrobe) (La Trobe Univers... 1137 citations

Publication Info

Year
2018
Type
article
Volume
78
Issue
11
Pages
15169-15211
Citations
1659
Access
Closed

Social Impact

Social media, news, blog, policy document mentions

Citation Metrics

1659
OpenAlex
53
Influential

Cite This

Hamed Jelodar, Yongli Wang, Chi Yuan et al. (2018). Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey. Multimedia Tools and Applications , 78 (11) , 15169-15211. https://doi.org/10.1007/s11042-018-6894-4

Identifiers

DOI
10.1007/s11042-018-6894-4
arXiv
1711.04305

Data Quality

Data completeness: 84%