Raspberry Pi 3 Cookbook for Python Programmers - Third Edition

How to do it...

1. Initialize a new Python file by importing the following file:

import numpy as np 
from nltk.corpus import brown 
from chunking import splitter 

2. Define the main function and read the input data from Brown corpus:

if __name__=='__main__': 
        content = ' '.join(brown.words()[:10000]) 

3. Split the text content into chunks:

    num_of_words = 2000 
    num_chunks = [] 
    count = 0 
    texts_chunk = splitter(content, num_of_words) 

4. Build a vocabulary based on these text chunks:

    for text in texts_chunk: 
      num_chunk = {'index': count, 'text': text} 
      num_chunks.append(num_chunk) 
      count += 1

5. Extract a document word matrix, which effectively counts the amount of incidences of each word in the document:

  from sklearn.feature_extraction.text       import ...