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Natural Language Processing with Python NLTK part 1 - Tokenizer

Natural Language Processing

Starting with the NLP articles first we will try the tokenizer in the NLTK package. Tokenizer breaks a paragraph into the relevant sub strings or sentences based on the tokenizer you used. In this I will use the Sent tokenizer, word_tokenizer and TweetTokenizer which has its specific work to do.


import nltk
from nltk.tokenize import sent_tokenize, word_tokenize, TweetTokenizer

para = "Hello there this is the blog about NLP. In this blog I have made some posts. " \
       "I can come up with new content."
tweet = "#Fun night. :) Feeling crazy #TGIF"

# tokenizing the paragraph into sentences and words
sent = sent_tokenize(para)
word = word_tokenize(para)

# printing the output
print("this paragraph has " + str(len(sent)) + " sentences and " + str(len(word)) + " words")

# print each sentence
k = 1
for i in sent:
    print("sentence " + str(k) + " = " + i)
    k += 1

# print each word
k = 1
for i in word:
    print("word " + str(k) + " = " + i)
    k += 1

# Comparing different kinds of tokenizer
print("Comparing word_tokenizer and TweetTokenizer")
print(word_tokenize(tweet))
print(TweetTokenizer().tokenize(tweet))

The Code first tokenize for sentences and words using sent_tokenizer and word_tokenizer. The output of this code will be as follows.


As you can see the sent_tokenizer has separated the para in to separate sentences and the word tokenizer into sub strings. Notice at the end I have given a comparison to Word_tokenizer and TweetTokenizer. See how smileys are taken as a one component in TweetTokenizer and two separate things in word_tokenizer.

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