topicmodels: An R Package for Fitting Topic Models

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Authors:	Bettina Grün, Kurt Hornik
Title:	topicmodels: An R Package for Fitting Topic Models
Abstract:	Topic models allow the probabilistic modeling of term frequency occurrences in documents. The fitted model can be used to estimate the similarity between documents as well as between a set of specified keywords using an additional layer of latent variables which are referred to as topics. The R package topicmodels provides basic infrastructure for fitting topic models based on data structures from the text mining package tm. The package includes interfaces to two algorithms for fitting topic models: the variational expectation-maximization algorithm provided by David M. Blei and co-authors and an algorithm using Gibbs sampling by Xuan-Hieu Phan and co-authors.
	Page views:: 22094. Submitted: 2010-09-23. Published: 2011-05-09.
Paper:	topicmodels: An R Package for Fitting Topic Models     [size=1em]Download PDF (Downloads: 22779)
Supplements:	topicmodels_0.1-0.tar.gz: R source package [size=1em]Download(Downloads: 742; 812KB) corpus.JSS.papers_2011.04.10.tar.gz: R source package with JSS corpus [size=1em]Download(Downloads: 727; 102KB) v40i13.R: R example code from the paper [size=1em]Download(Downloads: 913; 14KB) AP.rda: Cross validation simultation results in R binary format [size=1em]Download(Downloads: 711; 2KB) AP-40.rda: LDA fitting results in R binary format [size=1em]Download(Downloads: 762; 8MB)