Topic Modeling: A Comprehensive Review

• Main Authors: Pooja Kherwa, Poonam Bansal
• Format: Article
• Language: English
• Published: European Alliance for Innovation (EAI) 2020-01-01
• Series: EAI Endorsed Transactions on Scalable Information Systems
• Subjects: topic modeling; latent dirichlet allocation; latent semantic analysis; inference; dimension reduction
• Online Access: https://eudl.eu/pdf/10.4108/eai.13-7-2018.159623

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 itsapplications 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 challengesof topic modelling, which will definitely give researchers an insight for good research.