Biterm Topic Model

This is a simple Python implementation of the awesome Biterm Topic Model. This model is accurate in short text classification. It explicitly models the word co-occurrence patterns in the whole corpus to solve the problem of sparse word co-occurrence at document-level.

Simply install by:

pip install biterm

Load some short texts and vectorize them via sklearn.

    from sklearn.feature_extraction.text import CountVectorizer

    texts = open('./data/reuters.titles').read().splitlines()[:50]
    vec = CountVectorizer(stop_words='english')
    X = vec.fit_transform(texts).toarray()

Get the vocabulary and the biterms from the texts.

    from biterm.utility import vec_to_biterms

    vocab = np.array(vec.get_feature_names())
    biterms = vec_to_biterms(X)

Create a BTM and pass the biterms to train it.

    from biterm.cbtm import oBTM

    btm = oBTM(num_topics=20, V=vocab)
    topics = btm.fit_transform(biterms, iterations=100)

Save a topic plot using pyLDAvis and explore the results! (also see simple_btml.py)

    from biterm.btm import oBTM

    btm = oBTM(num_topics=20, V=vocab)
    topics = btm.fit_transform(biterms, iterations=100)

Inference is done with Gibbs Sampling and it's not really fast. The implementation is not meant for production. But if you have to classify a lot of texts you can try using online learning. Use the Cython version to speed up performance a bit.

import numpy as np
import pyLDAvis
from biterm.cbtm import oBTM 
from sklearn.feature_extraction.text import CountVectorizer
from biterm.utility import vec_to_biterms, topic_summuary # helper functions

if __name__ == "__main__":

    texts = open('./data/reuters.titles').read().splitlines()

    # vectorize texts
    vec = CountVectorizer(stop_words='english')
    X = vec.fit_transform(texts).toarray()

    # get vocabulary
    vocab = np.array(vec.get_feature_names())

    # get biterms
    biterms = vec_to_biterms(X)

    # create btm
    btm = oBTM(num_topics=20, V=vocab)

    print("\n\n Train Online BTM ..")
    for i in range(0, len(biterms), 100): # prozess chunk of 200 texts
        biterms_chunk = biterms[i:i + 100]
        btm.fit(biterms_chunk, iterations=50)
    topics = btm.transform(biterms)

    print("\n\n Visualize Topics ..")
    vis = pyLDAvis.prepare(btm.phi_wz.T, topics, np.count_nonzero(X, axis=1), vocab, np.sum(X, axis=0))
    pyLDAvis.save_html(vis, './vis/online_btm.html')

    print("\n\n Topic coherence ..")
    topic_summuary(btm.phi_wz.T, X, vocab, 10)

    print("\n\n Texts & Topics ..")
    for i in range(len(texts)):
        print("{} (topic: {})".format(texts[i], topics[i].argmax()))