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Tweet Sentiment Analysis of the 2020 U.S. Presidential Election

Published: 03 June 2021 Publication History

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.

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cover image ACM Conferences
WWW '21: Companion Proceedings of the Web Conference 2021
April 2021
726 pages
ISBN:9781450383134
DOI:10.1145/3442442
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 03 June 2021

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Author Tags

  1. 2020 U.S. Presidential Election
  2. Sentiment Analysis
  3. Tweet

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WWW '21
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WWW '21: The Web Conference 2021
April 19 - 23, 2021
Ljubljana, Slovenia

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  • (2024)Digital Democracy at Crossroads: A Meta-Analysis of Web and AI Influence on Global ElectionsCompanion Proceedings of the ACM Web Conference 202410.1145/3589335.3652003(1126-1129)Online publication date: 13-May-2024
  • (2024)Improving Classification Accuracy With Preprocessing Techniques For Sentiment Analysis2024 International Conference on Data Science and Its Applications (ICoDSA)10.1109/ICoDSA62899.2024.10651657(487-490)Online publication date: 10-Jul-2024
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