Research Article
Topic Modeling: A Comprehensive Review
@ARTICLE{10.4108/eai.13-7-2018.159623, author={Pooja Kherwa and Poonam Bansal}, title={Topic Modeling: A Comprehensive Review}, journal={EAI Endorsed Transactions on Scalable Information Systems}, volume={7}, number={24}, publisher={EAI}, journal_a={SIS}, year={2019}, month={7}, keywords={Topic Modeling, Latent Dirichlet Allocation, Latent Semantic Analysis, Inference, Dimension reduction}, doi={10.4108/eai.13-7-2018.159623} }- Pooja Kherwa
Poonam Bansal
Year: 2019
Topic Modeling: A Comprehensive Review
SIS
EAI
DOI: 10.4108/eai.13-7-2018.159623
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.
Copyright © 2019 Pooja Kherwa et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
