Abstract
U.S. President Joe Biden took his oath after being victorious in the controversial U.S. elections of 2020. The polls were conducted over postal ballot due to the coronavirus pandemic following delays of the announcement of the election's results. Donald J. Trump claimed that there was potential rigging against him and refused to accept the results of the polls. The sentiment analysis captures the opinions of the masses over social media for global events. In this work, we analyzed Twitter sentiment to determine public views before, during, and after elections and compared them with actual election results. We also compared opinions from the 2016 election in which Donald J. Trump was victorious with the 2020 election. We created a dataset using tweets' API, pre-processed the data, extracted the right features using TF-IDF, and applied the Naive Bayes Classifier to obtain public opinions. As a result, we identified outliers, analyzed controversial and swing states, and cross-validated election results against sentiments expressed over social media. The results reveal that the election outcomes coincide with the sentiment expressed on social media in most cases. The pre and post-election sentiment analysis results demonstrate the sentimental drift in outliers. Our sentiment classifier shows an accuracy of 94.58% and a precision of 93.19%.