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Authors: Bettina Grün, Kurt Hornik
Title: [download]
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topicmodels: An R Package for Fitting Topic Models
Reference: Vol. 40, Issue 13, May 2011
Submitted 2010-09-23, Accepted 2011-04-26
Type: Article
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.

Paper: [download]
(13773)
topicmodels: An R Package for Fitting Topic Models
(application/pdf, 724.5 KB)
Supplements: [download]
(621)
topicmodels_0.1-0.tar.gz: R source package
(application/x-gzip, 812.9 KB)
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(610)
corpus.JSS.papers_2011.04.10.tar.gz: R source package with JSS corpus
(application/x-gzip, 102.8 KB)
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v40i13.R: R example code from the paper
(application/octet-stream, 14.2 KB)
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AP.rda: Cross validation simultation results in R binary format
(application/octet-stream, 2.4 KB)
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AP-40.rda: LDA fitting results in R binary format
(application/octet-stream, 8.9 MB)
Resources: BibTeX | OAI
Creative Commons License
This work is licensed under the licenses
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.
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