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:
BTM_0.3.7.tar.gz
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BTM.pdf |BTM.html✨
BTM/json (API)
NEWS
| # Install 'BTM' in R: |
| install.packages('BTM', repos = c('https://bnosac.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/bnosac/btm/issues
biterm-topic-modellingnatural-language-processingtopic-modeling
Last updated 2 years agofrom:205ae029a1. Checks:OK: 4 NOTE: 5. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 06 2024 |
| R-4.5-win-x86_64 | NOTE | Nov 06 2024 |
| R-4.5-linux-x86_64 | NOTE | Nov 06 2024 |
| R-4.4-win-x86_64 | NOTE | Nov 06 2024 |
| R-4.4-mac-x86_64 | NOTE | Nov 06 2024 |
| R-4.4-mac-aarch64 | NOTE | Nov 06 2024 |
| R-4.3-win-x86_64 | OK | Nov 06 2024 |
| R-4.3-mac-x86_64 | OK | Nov 06 2024 |
| R-4.3-mac-aarch64 | OK | Nov 06 2024 |
Exports:BTM
Dependencies:Rcpp
