BTM: Biterm Topic Models for Short Text

Biterm Topic Models find topics in collections of short texts. It is a word co-occurrence based topic model that learns topics by modeling word-word co-occurrences patterns which are called biterms. This in contrast to traditional topic models like Latent Dirichlet Allocation and Probabilistic Latent Semantic Analysis which are word-document co-occurrence topic models. A biterm consists of two words co-occurring in the same short text window. This context window can for example be a twitter message, a short answer on a survey, a sentence of a text or a document identifier. The techniques are explained in detail in the paper 'A Biterm Topic Model For Short Text' by Xiaohui Yan, Jiafeng Guo, Yanyan Lan, Xueqi Cheng (2013) <>.

Version: 0.3.7
Imports: Rcpp, utils
LinkingTo: Rcpp
Suggests: udpipe, data.table
Published: 2023-02-11
DOI: 10.32614/CRAN.package.BTM
Author: Jan Wijffels [aut, cre, cph] (R wrapper), BNOSAC [cph] (R wrapper), Xiaohui Yan [ctb, cph] (BTM C++ library)
Maintainer: Jan Wijffels <jwijffels at>
License: Apache License 2.0
NeedsCompilation: yes
Materials: README NEWS
In views: NaturalLanguageProcessing
CRAN checks: BTM results


Reference manual: BTM.pdf


Package source: BTM_0.3.7.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): BTM_0.3.7.tgz, r-oldrel (arm64): BTM_0.3.7.tgz, r-release (x86_64): BTM_0.3.7.tgz, r-oldrel (x86_64): BTM_0.3.7.tgz
Old sources: BTM archive

Reverse dependencies:

Reverse suggests: oolong, textplot


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