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NLP | Categorized Text Corpus

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  • Last Updated : 26 Nov, 2021

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']

 


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