A COMPREHENSIVE STUDY ON SENTIMENT ANALYSIS

Abstract

Sentiment Analysis (SA), also known as opinion mining, is a significant subfield of Natural Language Processing (NLP) that focuses on extracting subjective information from textual data. With the rapid growth of user-generated content on digital platforms, SA has become essential for understanding public opinion, customer feedback, and social trends. This paper presents a comprehensive study of sentiment analysis, including its methodologies, datasets, applications, challenges, and future directions. A comparative analysis of different techniques such as lexicon-based, machine learning, and deep learning approaches is provided. The study highlights recent advancements in transformer-based models and discusses research gaps in the field. Keywords: Sentiment Analysis, Opinion Mining, Natural Language Processing, Machine Learning, Deep Learning, Text Classification