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A Study of Topic Modeling Methods

Topic model provides an easy means to analyze huge amount of untagged text as well as other data. A topic can be defined as a group of words that happen to occur together at a greater frequency. Topic models connects words that have similar kind of meanings and differentiate among words with different or multiple meanings. So, topic models in simple words are a set of algorithms that unveil the hidden thematic structure in a document collection. It allows us to order, search and outline different large records of texts. In this paper we present a survey on different topic modeling techniques which includes Latent Semantic Analysis (LSA), Probabilistic Latent Semantic Analysis (PLSA), and Latent Dirichlet Allocation (LDA) along with some of the extensions of LDA. The characteristics, limitations and applications of these topic modeling techniques are also studied and summarized.