Topics extraction with Non-Negative Matrix Factorization

This is a proof of concept application of Non Negative Matrix Factorization of the term frequency matrix of a corpus of documents so as to extract an additive model of the topic structure of the corpus.

The default parameters (n_samples / n_features / n_topics) should make the example runnable in a couple of tens of seconds. You can try to increase the dimensions of the problem be ware than the time complexity is polynomial.

Here are some sample extracted topics that look quite good:

Topic #0: god people bible israel jesus christian true moral think christians believe don say human israeli church life children jewish

Topic #1: drive windows card drivers video scsi software pc thanks vga graphics help disk uni dos file ide controller work

Topic #2: game team nhl games ca hockey players buffalo edu cc year play university teams baseball columbia league player toronto

Topic #3: window manager application mit motif size display widget program xlib windows user color event information use events x11r5 values

Topic #4: pitt gordon banks cs science pittsburgh univ computer soon disease edu reply pain health david article medical medicine 16

Python source code: topics_extraction_with_nmf.py

# Author: Olivier Grisel <olivier.grisel@ensta.org>
# License: Simplified BSD

from time import time
from sklearn.feature_extraction import text
from sklearn import decomposition
from sklearn import datasets

n_samples = 1000
n_features = 1000
n_topics = 10
n_top_words = 20

# Load the 20 newsgroups dataset and vectorize it using the most common word
# frequency with TF-IDF weighting (without top 5% stop words)

t0 = time()
print "Loading dataset and extracting TF-IDF features..."
dataset = datasets.fetch_20newsgroups(shuffle=True, random_state=1)

vectorizer = text.CountVectorizer(max_df=0.95, max_features=n_features)
counts = vectorizer.fit_transform(dataset.data[:n_samples])
tfidf = text.TfidfTransformer().fit_transform(counts)
print "done in %0.3fs." % (time() - t0)

# Fit the NMF model
print "Fitting the NMF model on with n_samples=%d and n_features=%d..." % (
    n_samples, n_features)
nmf = decomposition.NMF(n_components=n_topics).fit(tfidf)
print "done in %0.3fs." % (time() - t0)

# Inverse the vectorizer vocabulary to be able
feature_names = vectorizer.get_feature_names()

for topic_idx, topic in enumerate(nmf.components_):
    print "Topic #%d:" % topic_idx
    print " ".join([feature_names[i]
                    for i in topic.argsort()[:-n_top_words - 1:-1]])
    print