Twitter Sentiment Analysis using Machine Learning

Abstract

Social media makes it easier for people to communicate with one another online. Social media encompasses a wide range of applications and platforms, including Facebook for entertainment, Instagram for youth, Twitter for social and political, and YouTube, that let users share information, communicate online, and create communities. More than 4.7 billion individuals, or nearly 60% of the world's population, utilise social media. Twitter is a popular social media platform where users may express their feelings and opinions. In order to determine user sentiments, this Twitter sentiment analysis study uses sentiment analysis to data from tweets on the social media site. A whole new set of problems, such as the usage of slang and acronyms, are brought about by the relatively small size of the tweet format. Our objective is to carry out research on Twitter sentiment analysis while outlining the methodology, models, and generalised Python-based approach that was employed.