NLP | Categorized Text Corpus

If we have a large number of text data, then one can categorize it to separate sections. 

Code #1 : Categorization

Python3

# Loading brown corpus
from nltk.corpus import brown

brown.categories()

Output : 

['adventure', 'belles_lettres', 'editorial', 'fiction', 'government',
'hobbies', 'humor', 'learned', 'lore', 'mystery', 'news', 'religion',
'reviews', 'romance', 'science_fiction']

How to do categorize a corpus?
Easiest way is to have one file for each category. The following are two excerpts from the movie_reviews corpus: 

• movie_pos.txt
• movie_neg.txt

Using these two files, we’ll have two categories – pos and neg.

Code #2 : Let’s categorize

Python3

from nltk.corpus.reader import CategorizedPlaintextCorpusReader

reader = CategorizedPlaintextCorpusReader(
        '.', r'movie_.*\.txt', cat_pattern = r'movie_(\w+)\.txt')

print ("Categorize : ", reader.categories())

print ("\nNegative field : ", reader.fileids(categories =['neg']))

print ("\nPositive field : ", reader.fileids(categories =['pos']))

Output : 

Categorize : ['neg', 'pos']

Negative field : ['movie_neg.txt']

Positive field : ['movie_pos.txt']

Code #3 : Instead of cat_pattern, using in a cat_map

Python3

from nltk.corpus.reader import CategorizedPlaintextCorpusReader

reader = CategorizedPlaintextCorpusReader(
        '.', r'movie_.*\.txt', cat_map ={'movie_pos.txt': ['pos'],
                                        'movie_neg.txt': ['neg']})

print ("Categorize : ", reader.categories())

Output : 

Categorize : ['neg', 'pos']