Word tokenization is the process of splitting sentences into their constituent words. This also includes splitting standard contractions (e.g., it's becomes "it" and "a") and treating punctuation marks (like commas, single quotes, and periods followed by white-space) as separate tokens. In below code snippet, we would look at different ways of word tokenize using NLTK and TextBlob library.

Word Tokenizer

import nltk
from textblob import TextBlob


data = "Natural language is a central part of our day to day life, and it's so interesting to work on any problem related to languages."

nltk_output = nltk.word_tokenize(data)
textblob_output = TextBlob(data).words

print(nltk_output)
print(textblob_output)

['Natural', 'language', 'is', 'a', 'central', 'part', 'of', 'our', 'day', 'to', 'day', 'life', ',', 'and', 'it', "'s", 'so', 'interesting', 'to', 'work', 'on', 'any', 'problem', 'related', 'to', 'languages', '.']
['Natural', 'language', 'is', 'a', 'central', 'part', 'of', 'our', 'day', 'to', 'day', 'life', 'and', 'it', "'s", 'so', 'interesting', 'to', 'work', 'on', 'any', 'problem', 'related', 'to', 'languages']