Twitter Sentiment Analysis-Based Classification Model Incorporating Hybrid Naive Bayes Classifiers

Sentiment analysis is the process of using computers to identify and categorize different points of view that are expressed in a text. The main goal of this process is to ascertain the writer’s stance that is whether positive, negative, or neutral about a given subject, item, etc. Users of the social networking and microblogging platform Twitter can publish quick status updates. The process of computationally identifying and categorizing the many points of view expressed in a text is known as analysis, and it primarily aims to ascertain the author’s stance whether favorable, negative, or neutral about a given subject, item, etc. Users of the social networking and microblogging platform Twitter can publish quick status updates. Because of the popularity of social media, a lot of individuals send messages, such as testimonials, remarks, and thoughts. Sentiment analysis, to put it simply, is the process of gathering information from a source and using natural language processing, computational linguistics, and text analysis to inform judgements. Many apps, particularly those used by businesses, try to determine how well customers accept their products. This project aims to develop a useful classifier that is capable of reliably and automatically classifying the sentiment of an unlabeled stream of tweets. By classifying tweets into three categories positive, negative, and neutral, this project seeks to address the problem of sentiment analysis on Twitter.