Published Paper Details:

This project tackles the issue of tweet sentiment analysis, which involves categorizing tweets into those that indicate good, negative, or neutral mood. Twitter is a social networking and microblogging website that enables users to post 140-character maximum status updates With over 200 million registered users, of which 100 million are active users and half of them log in at least daily, it is a service that is rapidly growing. Each day, it generates approximately 250 million tweets. We intend to reflect the public opinion by assessing the feelings stated in the tweets in light of this significant usage. Numerous applications require the analysis of public mood, including businesses attempting to gauge the market response to their products, the prediction of political outcomes, and the analysis of socioeconomic phenomena like stock exchange. The goal of this project is to create a practical classifier that can accurately and automatically identify the sentiment of an unidentified tweet stream.