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Coronavirus Pandemic (COVID-19): Emotional Toll Analysis on Twitter

Author

Listed:
  • Jalal S. Alowibdi

    (University of Jeddah, Saudi Arabia)

  • Abdulrahman A. Alshdadi

    (University of Jeddah, Saudi Arabia)

  • Ali Daud

    (University of Jeddah, Saudi Arabia)

  • Mohamed M. Dessouky

    (University of Jeddah, Saudi Arabia)

  • Essa Ali Alhazmi

    (Jazan University, Saudi Arabia)

Abstract

People are afraid about COVID-19 and are actively talking about it on social media platforms such as Twitter. People are showing their emotions openly in their tweets on Twitter. It's very important to perform sentiment analysis on these tweets for finding COVID-19's impact on people's lives. Natural language processing, textual processing, computational linguists, and biometrics are applied to perform sentiment analysis to identify and extract the emotions. In this work, sentiment analysis is carried out on a large Twitter dataset of English tweets. Ten emotional themes are investigated. Experimental results show that COVID-19 has spread fear/anxiety, gratitude, happiness and hope, and other mixed emotions among people for different reasons. Specifically, it is observed that positive news from top officials like Trump of chloroquine as cure to COVID-19 has suddenly lowered fear in sentiment, and happiness, gratitude, and hope started to rise. But, once FDA said, chloroquine is not effective cure, fear again started to rise.

Suggested Citation

  • Jalal S. Alowibdi & Abdulrahman A. Alshdadi & Ali Daud & Mohamed M. Dessouky & Essa Ali Alhazmi, 2021. "Coronavirus Pandemic (COVID-19): Emotional Toll Analysis on Twitter," International Journal on Semantic Web and Information Systems (IJSWIS), IGI Global, vol. 17(2), pages 1-21, April.
    • Handle: RePEc:igg:jswis0:v:17:y:2021:i:2:p:1-21
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    Cited by:

    1. Gaurav, Akshat & Gupta, Brij B. & Panigrahi, Prabin Kumar, 2022. "A novel approach for DDoS attacks detection in COVID-19 scenario for small entrepreneurs," Technological Forecasting and Social Change, Elsevier, vol. 177(C).
    2. Cano-Marin, Enrique & Mora-Cantallops, Marçal & Sanchez-Alonso, Salvador, 2023. "The power of big data analytics over fake news: A scientometric review of Twitter as a predictive system in healthcare," Technological Forecasting and Social Change, Elsevier, vol. 190(C).
    3. Kumari, Pooja & Shankar, Amit & Behl, Abhishek & Pereira, Vijay & Yahiaoui, Dorra & Laker, Benjamin & Gupta, Brij B. & Arya, Varsha, 2024. "Investigating the barriers towards adoption and implementation of open innovation in healthcare," Technological Forecasting and Social Change, Elsevier, vol. 200(C).

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