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Python Textmining Package

Overview

This package contains a variety of useful functions for text mining in Python. It focuses on statistical text mining (i.e. the bag-of-words model) and makes it very easy to create a term-document matrix from a collection of documents. This matrix can then be read into a statistical package (R, MATLAB, etc.) for further analysis. The package also provides some useful utilities for finding collocations (i.e. significant two-word phrases), computing the edit distance between words, and chunking long documents up into smaller pieces.

The package has a large amount of curated data (stopwords, common names, an English dictionary with parts of speech and word frequencies) which allows the user to extract fairly sophisticated features from a document.

This package does NOT have any natural language processing capabilities such as part-of-speech tagging. Please see the Python NLTK for that sort of functionality (plus much, much more).

Installation

The latest version (1.0) is available from the Python Package Index.

To install, either run pip install textmining or download and extract the .zip file and run python setup.py install.

Examples

The most common use of the textmining package is to create a term-document matrix for analysis with a statistical package such as R or MATLAB. Here is a simple example:

import textmining

def termdocumentmatrix_example():
    # Create some very short sample documents
    doc1 = 'John and Bob are brothers.'
    doc2 = 'John went to the store. The store was closed.'
    doc3 = 'Bob went to the store too.'
    # Initialize class to create term-document matrix
    tdm = textmining.TermDocumentMatrix()
    # Add the documents
    tdm.add_doc(doc1)
    tdm.add_doc(doc2)
    tdm.add_doc(doc3)
    # Write out the matrix to a csv file. Note that setting cutoff=1 means
    # that words which appear in 1 or more documents will be included in
    # the output (i.e. every word will appear in the output). The default
    # for cutoff is 2, since we usually aren't interested in words which
    # appear in a single document. For this example we want to see all
    # words however, hence cutoff=1.
    tdm.write_csv('matrix.csv', cutoff=1)
    # Instead of writing out the matrix you can also access its rows directly.
    # Let's print them to the screen.
    for row in tdm.rows(cutoff=1):
        print row

In addition to writing the term-document matrix to a CSV file, this code also prints the rows of the matrix to the screen:

['and', 'the', 'brothers', 'to', 'are', 'closed', 'bob', 'john', 'was', 'went', 'store', 'too']
[1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0]
[0, 2, 0, 1, 0, 1, 0, 1, 1, 1, 2, 0]
[0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1]

Please see the 'examples' directory in the package file for other sample applications.