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Tweeting During the Covid-19 Pandemic: Sentiment Analysis of Twitter Messages by President Trump

Published: 09 November 2020 Publication History

Abstract

In this article, we utilize VADER, a rule-based model, to perform sentiment analysis of tweets by President Donald Trump during the early spread of the Covid-19 pandemic across the United States, making it the worst-hit country in the world. We discover a statistically significant negative correlation between the sentiment of his messages and the number of Covid-19 cases in the United States, indicating an effect on the tone of his tweets as the pandemic took its toll on American lives and economy. Furthermore, we also witness a gradual shift from positive to negative sentiment in his messages mentioning China and coronavirus together.

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    Published In

    cover image Digital Government: Research and Practice
    Digital Government: Research and Practice  Volume 2, Issue 1
    COVID-19 Commentaries
    January 2021
    116 pages
    EISSN:2639-0175
    DOI:10.1145/3434277
    Issue’s Table of Contents
    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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    Association for Computing Machinery

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    Publication History

    Published: 09 November 2020
    Online AM: 30 October 2020
    Accepted: 01 October 2020
    Revised: 01 August 2020
    Received: 01 July 2020
    Published in DGOV Volume 2, Issue 1

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    Author Tags

    1. Covid-19
    2. Donald Trump
    3. Sentiment analysis
    4. Twitter
    5. VADER
    6. rule-based model

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    • (2024)Application of Machine Learning Algorithms for Analyzing Sentiments Using Twitter Dataset2024 2nd International Conference on Sustainable Computing and Smart Systems (ICSCSS)10.1109/ICSCSS60660.2024.10624990(1392-1397)Online publication date: 10-Jul-2024
    • (2024)Using Non-textual Content of Tweets in Sentiment Analysis: A Data Pre-processing ApproachApplied Engineering and Innovative Technologies10.1007/978-3-031-70760-5_6(72-82)Online publication date: 14-Dec-2024
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    • (2023)Optimal Machine Learning Driven Sentiment Analysis on COVID-19 Twitter DataComputers, Materials & Continua10.32604/cmc.2023.03340675:1(81-97)Online publication date: 2023
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    • (2021)COVID-19 Pandemic Tweets by Iranian Political Elites: A Content Analysis StudyDepiction of Health10.34172/doh.2021.2912:4(298-309)Online publication date: 22-Dec-2021
    • (2021)Sentiment analysis of Twitter texts using Machine learning algorithmsAcademic Platform Journal of Engineering and Science10.21541/apjes.9393389:3(460-471)Online publication date: 30-Sep-2021
    • (2021)The evolving role of preprints in the dissemination of COVID-19 research and their impact on the science communication landscapePLOS Biology10.1371/journal.pbio.300095919:4(e3000959)Online publication date: 2-Apr-2021

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