#Code of Bursty Biterm Topic Model
Bursty biterm topic model (BurstyBTM) is a topic model for bursty discovery in short text streams such as microblogs.
More detail can be referred to the following paper:
Xiaohui Yan, Jiafeng Guo, Yanyan Lan, Jun Xu, and Xueqi Cheng. A Probabilistic Model for Bursty Topic Discovery in Microblogs. AAAI2015.
The code includes a runnable example, you can run it by:
$ cd script $ ./runExample.sh
It trains BTM over the documents in sample-data/0.txt, 1.txt, ... and output the topics. The n.txt contains the training documents in day n, where each line represents one document with words separated by space as:
word1 word2 word3 ....
(Note: the sample data is only used for illustration of the usage of the code. It is not the data set used in the paper.)
You can change the paths of data files and parameters in script/runExample.sh to run over your own data.
Indeed, the runExample.sh processes the input documents in 4 steps.
1. Index the words in the documents
To simplify the main code, we provide a python script to map each word to a unique ID (starts from 0) in the documents.
$ python script/indexDocs.py <doc_pt> <dwid_pt> <voca_pt> doc_ptinput docs to be indexed, each line is a doc with the format "word word ..." dwid_pt output docs after indexing, each line is a doc with the format "wordId wordId ..." voca_pt output vocabulary file, each line is a word with the format "wordId word"
2. Statistic daily frequencies for each biterm
$ python bitermDayFreq.py <dwid_dir<res_dir> dwid_pt input docs, each line is a doc with the format "word word ..." res_pt output the frequencies of the biterms in the format "w1 w2freq"
3. Compute eta (bursty probability) in each day for biterms
$ python eta.py <n_day> <bf_dir> <res_dir> n_day number of days to be processed (count from 0) bf_pt input docs, each line is a biterm with its daily frequency. Line format: "w1 w2 day:freq day:freq ..." res_dir output the eta of the biterms. Line format: "w1 w2 eta"
4. Topic learning
The next step is to train the model using the documents represented by word ids.
$./src/bbtm <K> <W> <alpha> <beta> <n_iter> <biterm_pt> <model_dir> <fix_b> type 's' means simplified BurstyBTM, 'n' means normal BurstyBTM K int, number of topics, like 20 W int, the size of vocabulary alpha double, Symmetric Dirichlet prior of P(z), like 1 beta double, Symmetric Dirichlet prior of P(w|z), like 0.01 n_iter int, number of iterations of Gibbs sampling biterm_pt string, path of training biterms, each line is a biterm with the format 'wi wj eta' model_dir string, output directory fix_b 'y' means fixing the background word distribution to the empirical word distribution
The results will be written into the directory "model_dir":
- k20.day1.type-n.iter100.pw_z: a K*M matrix for P(w|z), suppose K=20, day=1, iter=100, and did not use the simplified model
- k20.day1.type-n.iter100.pz: a K*1 matrix for P(z), suppose K=20
5. Results display
Finally, we also provide a python script to illustrate the top words of the topics and their proportions in the collection.
$ python topicDisplay.py <model_dir> <voca_pt> model_dir the output dir of BTM K the number of topics voca_pt the vocabulary file
- 2015-01-13, v0.5, clean up
- 2014-09-25, v0.1