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.
- Website: https://github.com/bnosac/BTM
- Licenses: ASL 2.0
- Package source: gnu/packages/cran.scm
- Builds: See build status
- Issues: See known issues
r-btm 0.3.6 as follows:
guix install email@example.com
Or install the latest version:
guix install r-btm
You can also install packages in augmented, pure or containerized environments for development or simply to try them out without polluting you user profile. See the
guix shell documentation for more information.