Our implementation of Biterm Topic Model (BTM), as described in WWW 2013 paper: A biterm topic model for short texts
The BTM is a kind of short text topic model.
This repository doesn't contain the preprocess steps. So if you want to use this code, you should prepare the data by yourself.
The data format is described as follows:
word word word
Each line represents a document, the words in document are separated by a single blank space.
beta: the hyper-parameter beta, and the alpha is calculated as 50/numTopic.
num_iter: the number of iteration for gibbs sampling progress.
*_pdz.txt: the topic-level representation for each document. Each line is a topic distribution for one document. This is used for classification task.
*_phi.txt: the word-level representation for each topic. Each line is a word distribution for one topic. This is used for PMI Coherence task.
*_words.txt: word, wordID map information.