Tokenizing text data

When we deal with text, we need to break it down into smaller pieces for analysis. This is where tokenization comes into the picture. It is the process of dividing the input text into a set of pieces like words or sentences. These pieces are called tokens. Depending on what we want to do, we can define our own methods to divide the text into many tokens. Let's take a look at how to tokenize the input text using NLTK.

Create a new Python file and import the following packages:

from nltk.tokenize import sent_tokenize, \ 
        word_tokenize, WordPunctTokenizer 

Define some input text that will be used for tokenization:

# Define input text 
input_text = "Do you know how tokenization works? It's actually quite interesting! Let's analyze a couple of sentences and figure it out."  

Divide the input text into sentence tokens:

# Sentence tokenizer  
print("\nSentence tokenizer:") 
print(sent_tokenize(input_text)) 

Divide the input text into...