Code for Bursty Biterm Topic Model (published in AAAI2015)
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README.md

README.md

#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.

Usage

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

Related codes

History

  • 2015-01-13, v0.5, clean up
  • 2014-09-25, v0.1

If there is any question, feel free to contact: Xiaohui Yan(xhcloud@gmail.com).