Authors:
Kalpdrum Passi
and
Rakshit Sorathiya
Affiliation:
School of Engineering and Computer Science, Laurentian University, 935 Ramsey Lake Rd., Sudbury, ON, Canada
Keyword(s):
U.S. Elections 2020, Data Mining, Lexical Analysis, US Tweets, Word Tokenization, Word Cloud, Donald Trump, Joe Biden, Electoral College, US Voting/Elections.
Abstract:
The availability of internet services in the United States and rest of the world in general in the modern past has contributed to more traction in the social network platforms like Facebook, Twitter, YouTube, and much more. This has made it possible for individuals to freely speak and express their sentiments and emotions towards the society. In 2020, the United State Presidential Elections saw around 1.5 million tweets on Twitter specifically for the Democratic and Republican party, Joe Biden, and Donald Trump, respectively. The tweets involve people’s sentiments and opinions towards the two political leaders (Joe Biden and Donald Trump) and their parties. The study of beliefs, sentiments, perceptions, views, and feelings conveyed in text is known as sentiment analysis. The political parties have used this technique to run their campaigns and understand the opinions of the public. In this thesis, during the voting time for the United States Elections in 2020, we conducted text minin
g on approximately 1.5 million tweets received between 15th October and 8th November that address the two mainstream political parties in the United States. We aimed at how Twitter users perceived for both political parties and their candidates in the United States (Democratic and Republican Party) using VADER a sentiment analysis tool that is tailored to discover the social media emotions, with a lexicon and rule-based sentiment analysis. The results of the research were the Democratic Party’s Joe Biden regardless of the sentiments and opinions in the in Twitter showing Donald Trump could win.
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