Documentation
TOM (TOpic Modeling) is a Python library for topic modeling and browsing. Its objective is to allow for an efficient analysis of a text corpus from start to finish, via the discovery of latent topics. To this end, TOM features functions for preparing and vectorizing a text corpus. It also offers a common interface for two topic models (namely LDA using either variational inference or Gibbs sampling, and NMF using alternating least-square with a projected gradient method), and implements three state-of-the-art methods for estimating the optimal number of topics to model a corpus. What is more, TOM constructs an interactive Web-based browser that makes it easy to explore a topic model and the related corpus.
###Load and prepare a text corpus
The following code snippet shows how to load a corpus of French documents, lemmatize them and vectorize them using tf-idf with unigrams.
corpus = Corpus(source_file_path='input/raw_corpus.csv',
language='french',
vectorization='tfidf',
n_gram=1,
max_relative_frequency=0.8,
min_absolute_frequency=4,
preprocessor=FrenchLemmatizer())
print 'corpus size:', corpus.size
print 'vocabulary size:', len(corpus.vocabulary)
print 'Vector for document 0:\n', corpus.vector_for_document(0)
Here, we instantiate a NMF based topic model and generate plots with the three metrics for estimating the optimal number of topics to model the loaded corpus.
topic_model = NonNegativeMatrixFactorization(corpus)
viz = Visualization(topic_model)
viz.plot_greene_metric(min_num_topics=5,
max_num_topics=50,
tao=10, step=1,
top_n_words=10)
viz.plot_arun_metric(min_num_topics=5,
max_num_topics=50,
iterations=10)
viz.plot_consens_metric(min_num_topics=5,
max_num_topics=50,
iterations=10)
To allow reusing previously learned topics models, TOM can save them on disk, as shown below.
topic_model.infer_topics(num_topics=15)
utils.save_topic_model(topic_model, 'output/NMF_15topics.tom')
topic_model = utils.load_topic_model('output/NMF_15topics.tom')
This code excerpt illustrates how one can manipulate a topic model, e.g. get the topic distribution for a document or the word distribution for a topic.
print '\nTopics:', topic_model.print_topics(num_words=10)
print '\nTopic distribution for document 0:', \
topic_model.topic_distribution_for_document(0)
print '\nMost likely topic for document 0:', \
topic_model.most_likely_topic_for_document(0)
print '\nFrequency of topics:', \
topic_model.topics_frequency()
print '\nTop 10 most relevant words for topic 2:', \
topic_model.top_words(2, 10)
