Package: BTM 0.3.7

Jan Wijffels

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>.

Authors:Jan Wijffels [aut, cre, cph], BNOSAC [cph], Xiaohui Yan [ctb, cph]

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Bug tracker:https://github.com/bnosac/btm/issues

On CRAN:

biterm-topic-modellingnatural-language-processingtopic-modeling

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Last updated 2 years agofrom:205ae029a1. Checks:OK: 4 NOTE: 5. Indexed: yes.

Exports:BTM

Dependencies:Rcpp