An overview of topic modeling methods and tools

Abstract

Topic modeling is a powerful technique for analysis of a huge collection of a document. Topic modeling is used for discovering hidden structure from the collection of a document. The topic is viewed as a recurring pattern of co-occurring words. A topic includes a group of words that often occurs together. Topic modeling can link words with the same context and differentiate across the uses of words with different meanings. In this paper, we discuss methods of Topic Modeling which includes Vector Space Model (VSM), Latent Semantic Indexing (LSI), Probabilistic Latent Semantic Analysis (PLSA), Latent Dirichlet Allocation (LDA) with their features and limitations. After that, we will discuss tools available for topic modeling such as Gensim, Standford topic modeling toolbox, MALLET, BigARTM. Then some of the applications of Topic Modeling covered. Topic models have a wide range of applications like tag recommendation, text categorization, keyword extraction, information filtering and similarity search in the fields of text mining, information retrieval.