Of all the Social Media platforms, Twitter has gained a lot of traction these days. It has become a space for people to explicitly share their critiques and perspectives about the world. Trending political events often explode in a number of tweets, offering a unique possibility to gauge the relationship between expressed public sentiment and political activity. Following his defeat on November 3, 2020 in the US Presidential election, President Donald Trump used his Twitter handle to endorse questionable reports, and in the process endorsed several conspiracies and theories regarding outcome of the Presidential election. An article in The Washington Post claimed that on an average Trump posts more than fifty misleading claims a day. To overturn his 2020 presidential election defeat, on January 6, 2021, a boisterous group of Trump supporters, invited and openly incited by the President, stormed Capitol Hill to prevent and intimidate members of Congress who had gathered there to certify and validate the victory of President- elect Joseph Biden. This paper has attempted to analyse Twitter users’ sentiment in storming of the United States Capitol using R language and TM Package. Two thousand tweets were extracted from twitter users from USA which is then cleaned, processed, and categorized into ten sentiments finally summarizing the results as a whole. The nrc sentiment score method of the Syuzhet package has been used to classify sentiments and ‘ggplot2’ was used to visualize the sentiments obtained for this research.

Trump, US election, Twitter, Capitol Hill, Riots