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Probabilistic topic models are a suite of algorithms whose aim is to discover the hidden thematic structure in large archives of documents. In this article, we review the main ideas of this field, survey the current state-of-the-art, and describe some promising future directions. We first describe latent Dirichlet allocation (LDA)  , which is the simplest kind of topic model. We discuss its connections to probabilistic modeling, and describe two kinds of algorithms for topic discovery. Wedoi:10.1109/msp.2010.938079 pmid:25104898 pmcid:PMC4122269 fatcat:pignwt65obhyxinw4b4vfvstxi