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Title: Political analytics on election candidates and their parties in context of the US Presidential elections 2020
Authors: Sorathiya, Rakshit
Keywords: Sentiment analysis;Twitter;U.S. elections 2020;text mining;data mining;lexical analysis;positive polarity;negative polarity;tweets;word tokenization;word cloud;Donald Trump;Joe Biden;electoral college;voting/elections
Issue Date: 27-May-2021
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, Instagram, YouTube, and much more. This has made it possible for individuals to freely speak and express their sentiments and emotions towards the society. Social media has also made it possible for bringing people closer by making the world a global village. There are influencers who promote products on social media platforms and politicians run their campaigns online for broader reach. Social media has become the fuel for globalization. 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 computational study of beliefs, sentiments, evaluations, 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. It has also enabled the modification of their campaigns accordingly. In this thesis, during the voting time for the United States Elections in 2020, we conducted text mining 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 Party and Republican Party) using VADER (Valence Aware Dictionary and sEntiment Reasoner) 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.
Appears in Collections:Computational Sciences - Master's theses
Master's Theses

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