Analysis and visualization of COVID-19 discourse on Twitter using data science: a case study of the USA, the UK and India

Haider Ilyas, Ahmed Anwar, Ussama Yaqub, Zamil Alzamil, Deniz Appelbaum

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Purpose: This paper aims to understand, examine and interpret the main concerns and emotions of the people regarding COVID-19 pandemic in the UK, the USA and India using Data Science measures. Design/methodology/approach: This study implements unsupervised and supervised machine learning methods, i.e. topic modeling and sentiment analysis on Twitter data for extracting the topics of discussion and calculating public sentiment. Findings: Governments and policymakers remained the focus of public discussion on Twitter during the first three months of the pandemic. Overall, public sentiment toward the pandemic remained neutral except for the USA. Originality/value: This paper proposes a Data Science-based approach to better understand the public topics of concern during the COVID-19 pandemic.

Original languageEnglish
JournalGlobal Knowledge, Memory and Communication
DOIs
StateAccepted/In press - 2021

Keywords

  • COVID-19
  • Data science
  • Machine learning
  • Sentiment analysis
  • Topic modeling
  • Twitter

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