Python Text Processing - Tokenization

In Python tokenization basically refers to splitting up a larger body of text into smaller lines, words or even creating words for a non-English language. The various tokenization functions in-built into the nltk module itself and can be used in programs as shown below.

Line Tokenization

In the below example we divide a given text into different lines by using the function sent_tokenize.

main.py

import nltk
sentence_data = "The First sentence is about Python." + \
   "The Second: about Django. You can learn Python,Django and Data Ananlysis here. "
nltk_tokens = nltk.sent_tokenize(sentence_data)
print (nltk_tokens)

When we run the above program, we get the following output −

['The First sentence is about Python.', 
'The Second: about Django.', 
'You can learn Python,Django and Data Ananlysis here.']

Non-English Tokenization

In the below example we tokenize the German text.

main.py

import nltk

german_tokenizer = nltk.data.load('tokenizers/punkt/german.pickle')
german_tokens=german_tokenizer.tokenize('Wie geht es Ihnen?  Gut, danke.')
print(german_tokens)

Output

When we run the above program, we get the following output −

['Wie geht es Ihnen?', 'Gut, danke.']

Word Tokenzitaion

We tokenize the words using word_tokenize function available as part of nltk.

main.py

import nltk

word_data = "It originated from the idea that there are readers" + \
   "who prefer learning new skills from the comforts of their drawing rooms"
nltk_tokens = nltk.word_tokenize(word_data)
print (nltk_tokens)

Output

When we run the above program we get the following output −

['It', 'originated', 'from', 'the', 'idea', 'that', 'there', 'are', 'readers', 
'who', 'prefer', 'learning', 'new', 'skills', 'from', 'the',
'comforts', 'of', 'their', 'drawing', 'rooms']