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https://github.com/xiaohuiyan/BTM

Code for Biterm Topic Model (published in WWW 2013)
https://github.com/xiaohuiyan/BTM

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Code for Biterm Topic Model (published in WWW 2013)

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# Code of Biterm Topic Model



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).

(In constrast, LDA and PLSA are word-document co-occurrence topic models, since they model word-document co-occurrences.)



A biterm consists of two words co-occurring in the same context, for example, in the same short text window. Unlike LDA models the word occurrences, BTM models the biterm occurrences in a corpus. In generation procedure, a biterm is generated by drawn two words independently from a same topic. In other words, the distribution of a biterm b=(wi,wj) is defined as:



       P(b) = \sum_k{P(wi|z)*P(wj|z)*P(z)}.



With Gibbs sampling algorithm, we can learn topics by estimate P(w|k) and P(z).



More detail can be referred to the following paper:



> Xiaohui Yan, Jiafeng Guo, Yanyan Lan, Xueqi Cheng. A Biterm Topic Model For Short Text. WWW2013.



## Usage ##

The code has been test on linux. If you on windows, please install

cygwin (with bc, wc, make).



The code includes a runnable example, you can run it *on Linux* by:



       $ cd script

	   $ sh runExample.sh



It trains BTM over the documents in *sample-data/doc\_info.txt* and output the topics. The doc\_info.txt contains all the training documents, 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    input 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. Topic learning**  

   The next step is to train the model using the documents represented by word ids.    



    $ ./src/btm est         

      K	int, number of topics

      W	int, 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

      save_step	int, steps to save the results

      docs_pt	string, path of training docs

      model_dir	string, output directory

 

   The results will be written into the directory "model\_dir":   

   - k20.pw_z: a K*M matrix for P(w|z), suppose K=20   

   - k20.pz:   a K*1 matrix for P(z), suppose K=20



**3. Inference topic proportions for documents, i.e., P(z|d)**     

   If you need to analysis the topic proportions of each documents, just run the following common to infer that using the model estimated.



    $ ./src/btm inf    

      K	int, number of topics, like 20

      type	 string, 4 choices:sum_w, sum_b, lda, mix. sum_b is used in our  paper.

      docs_pt	string, path of docs to be inferred

      model_dir	string, output directory



   The result will be output to "model_dir":   

   - k20.pz_d: a N*K matrix for P(z|d), suppose K=20

  

**4. Results display**    

   Finally, we also provide a python script to illustrate the top words of the topics and their proportions in the collection. 



    $ python script/topicDisplay.py   

      model_dirthe output dir of BTM

      Kthe number of topics

      voca_ptthe vocabulary file



## Related codes ##

- [Online BTM](https://github.com/xiaohuiyan/OnlineBTM)

- [Bursty BTM](https://github.com/xiaohuiyan/BurstyBTM)



## History ##

- 2015-01-12, v0.5, improve the usability of the code

- 2012-09-25, v0.1



If there is any question, feel free to contact: [Xiaohui Yan](http://xiaohuiyan.github.io "Xiaohui Yan")([email protected]).