Topic Modeling: A Comprehensive Review

Authors

  • Pooja Kherwa Maharaja Surajmal Institute of Technology
  • Poonam Bansal Maharaja Surajmal Institute of Technology

DOI:

https://doi.org/10.4108/eai.13-7-2018.159623

Keywords:

Topic Modeling, Latent Dirichlet Allocation, Latent Semantic Analysis, Inference, Dimension reduction

Abstract

Topic modelling is the new revolution in text mining. It is a statistical technique for revealing the underlying semantic structure in large collection of documents. After analysing approximately 300 research articles on topic modeling, a comprehensive survey on topic modelling has been presented in this paper. It includes classification hierarchy, Topic modelling methods, Posterior Inference techniques, different evolution models of latent Dirichlet allocation (LDA) and its applications in different areas of technology including Scientific Literature, Bioinformatics, Software Engineering and analysing social network is presented. Quantitative evaluation of topic modeling techniques is also presented in detail for better understanding the concept of topic modeling. At the end paper is concluded with detailed discussion on challenges of topic modelling, which will definitely give researchers an insight for good research.

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Published

24-07-2019

How to Cite

1.
Kherwa P, Bansal P. Topic Modeling: A Comprehensive Review. EAI Endorsed Scal Inf Syst [Internet]. 2019 Jul. 24 [cited 2024 Nov. 14];7(24):e2. Available from: https://publications.eai.eu/index.php/sis/article/view/2142