Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
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:: 29111. Submitted: 2010-09-23. Published: 2011-05-09.
Paper: topicmodels: An R Package for Fitting Topic Models     Download PDF (Downloads: 32535)
Supplements:
topicmodels_0.1-0.tar.gz: R source package Download (Downloads: 954; 812KB)
corpus.JSS.papers_2011.04.10.tar.gz: R source package with JSS corpus Download (Downloads: 886; 102KB)
v40i13.R: R example code from the paper Download (Downloads: 1254; 14KB)
AP.rda: Cross validation simultation results in R binary format Download (Downloads: 910; 2KB)
AP-40.rda: LDA fitting results in R binary format Download (Downloads: 929; 8MB)
DOI: 10.18637/jss.v040.i13

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.