Paper Title
Impact of Topic Modelling Methods and Text Classification Techniques in Text Mining: A Survey

Abstract
The continuous growth of Information technology increases the amount of data explosively. Organize and analyse large document collection has become a big challenge. Text classifiers and topic models are used to sort out this problem. This paper mainly focuses on these two categories. First category discusses the three methods of topic modeling. They are Latent Semantic Analysis (LSA), Probabilistic Latent Semantic Analysis (PLSA) and Latent Dirichlet Allocation (LDA). The second category is text classification models. This includes Naïve Bayes Classifier, K-Nearest Neighbor and Support Vector Machines (SVM).A literature survey has done and explored the two categories. Finally, mentioned the combination of text classifiers and topic models can improve the classification accuracy. A combined approach of LDA and SVM show better performance than the others. Keywords - K- Nearest Neighbor, Latent Dirichlet Allocation, Latent Semantic Analysis, Naïve Bayes, Probabilistic Latent Semantic Analysis, Support Vector Machine, Topic Modeling, Text Classification.

Author - Mino George, P. Beaulah Soundarabai, Karthik Krishnamurthi

Published : Volume-4,Issue-3  ( Mar, 2017 )

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