January 6, 2021, was a noteworthy incident in the history of the United States of America. Dissidents attacked the Capitol building over the results of the 2020 presidential election. The purpose of this research is to understand how greater social media partisanship can create a greater division among many Americans leading to misinformation, political polarization, and mayhem in the society. The research analyzes queries generated through retrieved Twitter data on that specific data. In order to meet the study objective, a keyword analysis by using Python language programming and sentiment analysis was applied by using the Natural Language Processing method in the current study. According to the findings, the words such ‘jobs’, ‘vaccine’, ‘president’, ‘spread’ and ‘pandemic’ were the most dominant words in “retweets” and “likes” showing the concern of the society regarding the riot. Results also indicate words like ‘love’, ‘stock’, and ‘market’ were used in a positive manner to express the concerns while the words like ‘president’, ‘unemployment’, ‘arrest’, ‘covid’, and ‘mask’ were related to negative emotions. The research concludes that it is important to understand how social media could contribute to an attack on the Capitol building, the meeting place of the United States of America Congress and in order to better control this type of events, social media analysis outcomes can guide the society`s main areas of concerns.