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%.
Highlights
The U.S election, 2020 was a significant global event, as the Republican Party’s DonaldTrump was striving to secure his second term while Joe Biden of the Democratic Party expected to turn it around
We collected a dataset from Twitter for sentiment analysis of the 2020 U.S presidential elections
The data were collected before, during, and after the election to measure the public sentiment over social media, and it was compared with the actual election results
Summary
The U.S election, 2020 was a significant global event, as the Republican Party’s Donald. The margin of victory in the 2020 elections was much more prominent compared to 2000 Social media platforms such as Twitter, Instagram, and Facebook are common ways of expressing sentiments. Twitter is a much better place for sentiment analysis as compared to Facebook [17] In this sense, some people on social media might not be serious in really expressing their actual feelings. We created a unique dataset comprising pre and post-election tweets; In this work, we employed sentiment analysis over the Twitter dataset and compared it with the the 2020 U.S election results; The state with strong and weak sentiments for Donald Trump and Joe Biden were analyzed.
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