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textmine + lda in python

process corpus for lda

In a blog post I wrote about the python package lda, see here, I used the pre-processed data (included with the lda package) for the example. I have since received many questions regarding the document-term matrix, the titles, and the vocabulary-- where do they come from? This gist will use the textmining package to (hopefully) help answer these types of questions.

Install textmining package

To install textmining use pip (create a virtual environment first, if you'd like):

$ pip install textmining

Usage

Run script from command line

The script can be run from the command with the usual command:

$ python lda_textmine_ex.py

The output should look like:

**These are the 'documents', making up our 'corpus':
document 1: John and Bob are brothers.
document 2: John went to the store. The store was closed.
document 3: Bob went to the store too.
-- In real applications, these 'documents' might be read from files, websites, etc.

**These are the 'document titles':
title 1: Brothers.
title 2: John to the store.
title 3: Bob to the store.
-- In real applications, these 'titles' might be the file name, the story title, webpage title, etc.

** The textmining packages is one tool for creating the 'document-term' matrix, 'vocabulary', etc.
   You can write your own, if needed.

** Output produced by the textmining package...
* The 'document-term' matrix
type(X): <type 'numpy.ndarray'>
shape: (3, 12)
X:
[[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]]
-- Notice there are 3 rows, for 3 'documents' and
   12 columns, for 12 'vocabulary' words
-- The number of rows and columns depends on the number of documents
   and number of unique words in -all- documents

* The 'vocabulary':
type(vocab): <type 'tuple'>
len(vocab): 12
vocab:
('and', 'the', 'brothers', 'to', 'are', 'closed', 'bob', 'john', 'was', 'went', 'store', 'too')
-- These are the 12 words in the vocabulary
-- Often common 'stop' words, like 'and', 'the', 'to', etc are
   filtered out -before- creating the document-term matrix and vocab

* Again, the 'titles' for this 'corpus':
type(titles): <type 'tuple'>
len(titles): 3
titles:
('Brothers.', 'John to the store.', 'Bob to the store.')

Hopefully this gives a sense of how a set of documents (a corpus) relates to the document-term matrix, the vocabulary, and the titles mentioned in the original post.

#! /usr/bin/env python
# -*- coding: utf-8 -*-
# vim:fenc=utf-8
#
# Copyright © 2015 Christopher C. Strelioff <chris.strelioff@gmail.com>
#
# Distributed under terms of the MIT license.
"""
An example of getting titles and vocab for lda using textmine package.
-- adapted from: http://www.christianpeccei.com/textmining/
"""
from __future__ import print_function
import numpy as np
import textmining
# 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.'
print("\n**These are the 'documents', making up our 'corpus':")
for n, doc in enumerate([doc1, doc2, doc3]):
print("document {}: {}".format(n+1, doc))
print("-- In real applications, these 'documents' "
"might be read from files, websites, etc.")
# make a titles tuple
# -- these should be the "titles" for the "documents" above
titles = ("Brothers.",
"John to the store.",
"Bob to the store.")
print("\n**These are the 'document titles':")
for n, title in enumerate(titles):
print("title {}: {}".format(n+1, title))
print("-- In real applications, these 'titles' might "
"be the file name, the story title, webpage title, etc.")
# Initialize class to create term-document matrix
print("\n** The textmining packages is one tool for creating the "
"'document-term' matrix, 'vocabulary', etc."
"\n You can write your own, if needed.")
tdm = textmining.TermDocumentMatrix()
# Add the documents
tdm.add_doc(doc1)
tdm.add_doc(doc2)
tdm.add_doc(doc3)
# create a temp variable with doc-term info
temp = list(tdm.rows(cutoff=1))
# get the vocab from first row
vocab = tuple(temp[0])
# get document-term matrix from remaining rows
X = np.array(temp[1:])
##
## print out info, as in blog post with a little extra info
##
## post: http://bit.ly/1bxob2E
##
print("\n** Output produced by the textmining package...")
# document-term matrix
print("* The 'document-term' matrix")
print("type(X): {}".format(type(X)))
print("shape: {}".format(X.shape))
print("X:", X, sep="\n" )
print("-- Notice there are 3 rows, for 3 'documents' and\n"
" 12 columns, for 12 'vocabulary' words\n"
"-- The number of rows and columns depends on the number of documents\n"
" and number of unique words in -all- documents")
# the vocab
print("\n* The 'vocabulary':")
print("type(vocab): {}".format(type(vocab)))
print("len(vocab): {}".format(len(vocab)))
print("vocab:", vocab, sep="\n")
print("-- These are the 12 words in the vocabulary\n"
"-- Often common 'stop' words, like 'and', 'the', 'to', etc are\n"
" filtered out -before- creating the document-term matrix and vocab")
# titles for each story
print("\n* Again, the 'titles' for this 'corpus':")
print("type(titles): {}".format(type(titles)))
print("len(titles): {}".format(len(titles)))
print("titles:", titles, sep="\n", end="\n\n")
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