Topic modeling refers to an algorithm that explains “an observed corpus with a small set of distributions over terms” and “models for uncovering underlying semantic structure of a document collection” (Blei et al. 2003, Blei et al. 2009, Blei 2012). Several algorithms have been put forth to build a probabilistic topic model, e.g mixture-of-unigram (Nigam et al. 2000), Latent Semantic Indexing (Deerwester et al. 1990; Hofmann 1999) and Latent Dirichlet Allocation LDA (Blei et al. 2003). For more information, see Matthew Jockers and David Blei.
Interactive Text Mining Suite applies various LDA algorithms (topicmodels, lda and stm R packages). In addition, it allows users interactively choose number of topics, iterations and select the best models.
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