Paper Contents
Abstract
ABSTRACTSentiment Analysis, a key area within Natural Language Processing (NLP), focuses on identifying and extracting subjective information from text, such as emotions, opinions, and sentiments. This project, "Sentiment Analysis Using NLP," aims to analyze and categorize the sentiment embedded in textual data, which can be used to understand public opinion, gauge brand perception, monitor feedback, or detect emotional trends in social media. With the rapid growth of textual data on platforms like Twitter, Facebook, and product review sites, sentiment analysis has become crucial for businesses and organizations that wish to understand and respond to their audience's needsandconcerns..INTRODUCTIONSentiment analysis, also referred to as opinion mining, is a critical subfield of Natural Language Processing (NLP) that focuses on determining the sentiment expressed in a piece of text. With the increasing amount of textual data available from social media platforms, customer reviews, sentiment analysis has become an essential tool for businesses, researchers, and decision-makers.We explore the methodologies used in sentiment analysis and evaluates the efficacy of different NLP techniques, such as tokenization, part-of-speech tagging, and sentiment scoring, in classifying text data into positive, negative, or neutral sentiments.
Copyright
Copyright © 2024 Kiruba Devi M. This is an open access article distributed under the Creative Commons Attribution License.
