sis 20(24): e2

Research Article

Topic Modeling: A Comprehensive Review

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  • @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
Pooja Kherwa1,*, Poonam Bansal1
  • 1: Maharaja Surajmal Institute of Technology, C-4 Janak Puri. GGSIPU. New Delhi-110058.
*Contact email: poona281280@gmail.com

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.