imclab / pattern

Web mining module for Python, with tools for scraping, natural language processing, machine learning, network analysis and visualization.

Pattern

Pattern is a web mining module for the Python programming language. It bundles tools for:

• Data Mining: Google + Twitter + Wikipedia API, web crawler, HTML DOM parser
• Natural Language Processing: part-of-speech taggers, n-gram search, sentiment analysis, WordNet
• Machine Learning: vector space model, k-means clustering, Naive Bayes + k-NN + SVM classifiers
• Network Analysis: graph centrality and visualization.

It is well documented and bundled with 30+ examples and 350+ unit tests. The source code is licensed under BSD and available from http://www.clips.ua.ac.be/pages/pattern.

Version

2.6

License

BSD, see LICENSE.txt for further details.

Installation

Pattern is written for Python 2.5+ (no support for Python 3 yet). The module has no external dependencies except when using LSA in the pattern.vector module, which requires NumPy (installed by default on Mac OS X). To install Pattern so that it is available in all your scripts, unzip the download and from the command line do:

cd pattern-2.6
python setup.py install

If you have pip, you can automatically download and install from the PyPi repository:

pip install pattern

If none of the above works, you can make Python aware of the module in three ways:

• Put the pattern folder in the same folder as your script.
• Put the pattern folder in the standard location for modules so it is available to all scripts:
  • c:\python26\Lib\site-packages\ (Windows),
  • /Library/Python/2.6/site-packages/ (Mac OS X),
  • /usr/lib/python2.6/site-packages/ (Unix).
• Add the location of the module to sys.path in your script, before importing it:

MODULE = '/users/tom/desktop/pattern'
import sys; if MODULE not in sys.path: sys.path.append(MODULE)
from pattern.en import parsetree

Example

This example trains a classifier on adjectives mined from Twitter. First, tweets that contain hashtag #win or #fail are collected. For example: "$20 tip off a sweet little old lady today #win". The word part-of-speech tags are then parsed, keeping only adjectives. Each tweet is transformed to a vector: a dictionary of adjective → count items, labeled WIN or FAIL. The classifier uses the vectors to predict which adjectives are commonly associated with either WIN or FAIL.

from pattern.web    import Twitter
from pattern.en     import tag
from pattern.vector import KNN, count

knn = KNN()

for i in range(1, 3):
    for tweet in Twitter().search('#win OR #fail', start=i, count=100):
        s = tweet.text.lower()
        p = '#win' in s and 'WIN' or 'FAIL'
        v = tag(s)
        v = [word for word, pos in v if pos == 'JJ'] # JJ = adjective
        v = count(v) # {'sweet': 1}
        if v:
            knn.train(v, type=p)

print knn.classify('sweet')
print knn.classify('stupid')

Documentation

http://www.clips.ua.ac.be/pages/pattern

Reference

De Smedt, T., Daelemans, W. (2012). Pattern for Python. Journal of Machine Learning Research, 13, 2031–2035.

Contribute

The source code is hosted on GitHub and contributions or donations are welcomed, see the developer documentation. If you use Pattern in your work, please cite our reference paper.

Bundled dependencies

Pattern is bundled with the following data sets, algorithms and Python packages:

• Beautiful Soup, Leonard Richardson
• Brill tagger, Eric Brill
• Brill tagger for Dutch, Jeroen Geertzen
• Brill tagger for German, Gerold Schneider & Martin Volk
• Brill tagger for Spanish, trained on Wikicorpus (Samuel Reese & Gemma Boleda et al.)
• Brill tagger for French, trained on Lefff (Benoît Sagot & Lionel Clément et al.)
• Brill tagger for Italian, mined from Wiktionary
• English pluralization, Damian Conway
• Spanish verb inflection, Fred Jehle
• French verb inflection, Bob Salita
• Graph JavaScript framework, Aslak Hellesoy & Dave Hoover
• LIBSVM, Chih-Chung Chang & Chih-Jen Lin
• LIBLINEAR, Rong-En Fan et al.
• NetworkX centrality, Aric Hagberg, Dan Schult & Pieter Swart
• PDFMiner, Yusuke Shinyama
• PyWordNet, Oliver Steele
• simplejson, Bob Ippolito
• spelling corrector, Peter Norvig
• Universal Feed Parser, Mark Pilgrim
• WordNet, Christiane Fellbaum et al.

Acknowledgements

Authors:

• Tom De Smedt ([email protected])
• Walter Daelemans ([email protected])

Contributors (chronological):

• Frederik De Bleser
• Jason Wiener
• Daniel Friesen
• Jeroen Geertzen
• Thomas Crombez
• Ken Williams
• Peteris Erins
• Rajesh Nair
• F. De Smedt
• Radim Řehůřek
• Tom Loredo
• John DeBovis
• Thomas Sileo
• Gerold Schneider
• Martin Volk
• Samuel Joseph
• Shubhanshu Mishra
• Robert Elwell
• Fred Jehle
• Antoine Mazières + fabelier.org
• Rémi de Zoeten + closealert.nl
• Kenneth Koch
• Jens Grivolla
• Fabio Marfia
• Steven Loria
• Colin Molter + tevizz.com