Python Sentiment Analysis With the NLTK Library [With Examples]

Sentiment analysis is a technique to extract emotions from textual data. This data may be used to determine what people actually believe, think, and feel about specific subjects or products.

Python’s popularity as a programming language has resulted in a wide range of sentiment analysis applications. The Natural Language Toolkit (NLTK) is a common library for sentiment analysis.

In this tutorial, you will learn the fundamentals to perform sentiment analysis using Python’s NLTK library.

How to Install and Import the NLTK Library in Python

You must first know how to install and import the NLTK library into your Python distribution before you can begin sentiment analysis with NLTK.

Pip is the default Python package installer, which you can use to install NLTK. Enter the following command into your command prompt:

pip install nltk

Once the installation is completed, you can import NLTK into your python environment as shown below:

import nltk

Now you are good to go with NLTK sentiment analysis with Python.

Tokenization and Stop Words Removal with NLTK

We must first preprocess our text input before doing sentiment analysis.

The text must be modified, with stop words removed and words stemmed. NLTK offers several functions to achieve these objectives.

Let’s have a look at a few of these features:

How Do You Tokenize Text in Python?

Tokenization is the process of splitting text into discrete phrases or words. To do this, NLTK provides the word_tokenize() tokenizer part of the nltk.tokenize package.

A tokenizer converts a piece of text into a list of tokens and allows finding words and punctuation in the string.

The code snippet below uses a word tokenizer available in the NLTK library to split the given text into words.

from nltk.tokenize import word_tokenize

text = "Hello, today we will learn about Python…