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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) <https://github.com/xiaohuiyan/xiaohuiyan.github.io/blob/master/paper/BTM-WWW13.pdf>.

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 bnosac.be>
License: Apache License 2.0
URL: https://github.com/bnosac/BTM
NeedsCompilation: yes
Materials: README NEWS
In views: NaturalLanguageProcessing
CRAN checks: BTM results

Documentation:

Reference manual: BTM.pdf

Downloads:

Package source: BTM_0.3.7.tar.gz
Windows binaries: r-devel: BTM_0.3.7.zip, r-release: BTM_0.3.7.zip, r-oldrel: BTM_0.3.7.zip
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

Linking:

Please use the canonical form https://CRAN.R-project.org/package=BTM to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.