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.
| Identifer | oai:union.ndltd.org:VIENNA/oai:epub.wu-wien.ac.at:3987 |
| Date | January 2011 |
| Creators | Hornik, Kurt, Grün, Bettina |
| Publisher | American Statistical Association |
| Source Sets | Wirtschaftsuniversität Wien |
| Language | English |
| Detected Language | English |
| Type | Article, PeerReviewed |
| Format | application/pdf |
| Relation | http://www.jstatsoft.org/v40/i13, http://epub.wu.ac.at/3987/ |
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