Xiaohui Yan, Jiafeng Guo, Yanyan Lan, Xueqi Cheng. More detail can be referred to the following paper: Where estimates are provided for P(w|k)=phi and P(z)=theta. Estimation of the topic model is done with the Gibbs sampling algorithm.In other words, the distribution of a biterm b=(wi,wj) is defined as: P(b) = sum_k where k is the number of topics you want to extract. In the generation procedure, a biterm is generated by drawing two words independently from a same topic z. BTM models the biterm occurrences in a corpus (unlike LDA models which model the word occurrences in a document).A biterm consists of two words co-occurring in the same context, for example, in the same short text window.The Biterm Topic Model (BTM) is a word co-occurrence based topic model that learns topics by modeling word-word co-occurrences patterns (e.g., biterms) This R package is on CRAN, just install it with install.packages('BTM') What Topic modelling using biterms is particularly good for finding topics in short texts (as occurs in short survey answers or twitter data). This model models word-word co-occurrences patterns (e.g., biterms). This is an R package wrapping the C++ code available at for constructing a Biterm Topic Model (BTM). ![]() BTM - Biterm Topic Modelling for Short Text with R
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