NLTK & VADER Sentiment Analysis

Abstract

In the contemporary digital epoch, text analysis and text mining have emerged as pivotal components across a multitude of industries. Text analysis is defined as the systematic examination and extraction of significant insights from unstructured textual data. A particularly critical subfield within text analysis is sentiment analysis, which entails the assessment of the emotional tone conveyed within the text. Sentiment analysis encompasses a wide array of practical applications, ranging from brand surveillance to the evaluation of customer feedback. Python has gained recognition as a prominent programming language for the purposes of text analysis and mining, with the Natural Language Toolkit (NLTK) library being one of the most extensively utilized libraries for natural language processing within the Python ecosystem.This manuscript aims to elucidate a comprehensive, sequential methodology for conducting sentiment analysis through the utilization of the NLTK library in the Python programming language. Upon the conclusion of this manuscript, the reader will possess a robust comprehension of the methodologies involved in executing sentiment analysis with NLTK in Python, accompanied by a detailed example that may serve as a foundational reference for individual projects. Sentiment analysis helps in finding the emotional tone of a sentence. It helps businesses, researchers and developers to understand opinions and sentiments expressed in text data which is important for applications like social media monitoring, customer feedback analysis and more. One widely used tool for sentiment analysis is VADER which is a rule-based tool. In this article, we will see how to perform sentiment analysis using VADER in Python.