Abstract

This paper performs sentiment analysis of the political tweets in the US presidential elections 2020 centered around Biden and Trump, using the implementation of machine learning algorithms such as Decision Trees, Naive Bayes, Dummy Classifier, and Extreme Gradient Boosting. The present study shows how Naive Bayes can trace minute variations of sentiment about political discourses on social media. It follows that the best model among those analyzed is the Naive Bayes classifier (62% for Biden and 74% for Trump) on sentiment analysis in political tweets from the 2020 election, since it is a very instructive case of what public opinion was in the digital era.

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