Tweet Sentiment Analysis of the 2020 U.S. Presidential Election
Ethan Xia, Yue Han, Hongfu Liu
Abstract
In this paper, we conducted a tweet sentiment analysis of the 2020 U.S. Presidential Election between Donald Trump and Joe Biden. Specially, we identified the Multi-Layer Perceptron classifier as the methodology with the best performance on the Sanders Twitter benchmark dataset. We collected a sample of over 260,000 tweets related to the 2020 U.S. Presidential Election from the Twitter website via Twitter API, processed feature extraction, and applied Multi-Layer Perceptron to classify these tweets with a positive or negative sentiment. From the results, we concluded that (1) contrary to popular poll results, the candidates had a very close negative to positive sentiment ratio, (2) negative sentiment is more common and prominent than positive sentiment within the social media domain, (3) some key events can be detected by the trends of sentiment on social media, and (4) sentiment analysis can be used as a low-cost and easy alternative to gather political opinion.