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Detecting Emotional Evolution on Twitter during the COVID-19 Pandemic Using Text Analysis

Author

Listed:
  • Javier Cabezas

    (Data Science Laboratory, Rey Juan Carlos University, 28933 Móstoles, Spain)

  • Daniela Moctezuma

    (Centro de Investigación en Ciencias de Información Geoespacial, Tlalpan 14240, Mexico)

  • Alberto Fernández-Isabel

    (Data Science Laboratory, Rey Juan Carlos University, 28933 Móstoles, Spain)

  • Isaac Martín de Diego

    (Data Science Laboratory, Rey Juan Carlos University, 28933 Móstoles, Spain)

Abstract

Early in 2020, an unexpected and hazardous situation occurred threatening and challenging all of humankind. A new coronavirus called SARS-CoV-2 was first identified in Wuhan, China, and its related disease, called COVID-19, has induced one of the most dangerous crises at a global level since World War II. The ultra-fast transmission rate of the virus and the high mortality rate led the World Health Organization (WHO) to officially declare the situation a pandemic. Governments, for their part, were forced to implement unprecedented mobility restrictions and cease a large part of their economic activities. These facts triggered multiple reactions from people who expressed their feelings mainly through social networks (like Twitter), using them as vectors of information and opinion. In this paper, a study carried out in different Spanish speaking countries (Chile, Mexico, Peru, and Spain) is presented, which addresses the manner in which the evolution of the pandemic outbreak has affected the emotions expressed by individuals on Twitter over the last 13 months (from March 2020 to March 2021). We used a total of 3 million tweets to achieve this task. We made use of a well-known framework called EmoWeb to capture the dynamic variation in the sentimental value of pandemic-related words. The results reflect to what degree the pandemic and its derived problems have influenced and affected the population of the selected countries in different ways. The outcomes also illustrate the evolution over time of opinions published on Twitter regarding several topics related to COVID-19.

Suggested Citation

  • Javier Cabezas & Daniela Moctezuma & Alberto Fernández-Isabel & Isaac Martín de Diego, 2021. "Detecting Emotional Evolution on Twitter during the COVID-19 Pandemic Using Text Analysis," IJERPH, MDPI, vol. 18(13), pages 1-20, June.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:13:p:6981-:d:585062
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    References listed on IDEAS

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    1. Thomas Hale & Noam Angrist & Rafael Goldszmidt & Beatriz Kira & Anna Petherick & Toby Phillips & Samuel Webster & Emily Cameron-Blake & Laura Hallas & Saptarshi Majumdar & Helen Tatlow, 2021. "A global panel database of pandemic policies (Oxford COVID-19 Government Response Tracker)," Nature Human Behaviour, Nature, vol. 5(4), pages 529-538, April.
    2. Simon Porcher & Thomas Renault, 2021. "Social distancing beliefs and human mobility: Evidence from Twitter," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-12, March.
    3. Memon, Zaibunnisa & Qureshi, Sania & Memon, Bisharat Rasool, 2021. "Assessing the role of quarantine and isolation as control strategies for COVID-19 outbreak: A case study," Chaos, Solitons & Fractals, Elsevier, vol. 144(C).
    4. Javier M. Moguerza & Salvador Perelló Oliver & Isaac Martín de Diego & Víctor Aceña & Carmen Lancho & Marina Cuesta & César González Fernández, 2021. "Health Sufficiency Indicators for Pandemic Monitoring," IJERPH, MDPI, vol. 18(10), pages 1-21, May.
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    Cited by:

    1. Jingjing Gao & Gabriela A. Gallegos & Joe F. West, 2023. "Public Health Policy, Political Ideology, and Public Emotion Related to COVID-19 in the U.S," IJERPH, MDPI, vol. 20(21), pages 1-14, October.
    2. Gozuacik, Necip & Sakar, C. Okan & Ozcan, Sercan, 2023. "Technological forecasting based on estimation of word embedding matrix using LSTM networks," Technological Forecasting and Social Change, Elsevier, vol. 191(C).

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