Making sense of tweets using sentiment analysis on closely related topics

Abstract

Microblogging has taken a considerable upturn in recent years, with the growth of microblogging websites like Twitter people have started to share more of their opinions about various pressing issues on such online social networks. A broader understanding of the domain in question is required to make an informed decision. With this motivation, our study focuses on finding overall sentiments of related topics with reference to a given topic. We propose an architecture that combines sentiment analysis and community detection to get an overall sentiment of related topics. We apply that model on the following topics: shopping, politics, covid19 and electric vehicles to understand emerging trends, issues and its possible marketing, business and political implications.