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Using sentiment analysis to evaluate the impact of the COVID-19 outbreak on Italy’s country reputation and stock market performance

Author

Listed:
  • Gianpaolo Zammarchi

    (University of Cagliari)

  • Francesco Mola

    (University of Cagliari)

  • Claudio Conversano

    (University of Cagliari)

Abstract

During the recent Coronavirus disease 2019 (COVID-19) outbreak, the microblogging service Twitter has been widely used to share opinions and reactions to events. Italy was one of the first European countries to be severely affected by the outbreak and to establish lockdown and stay-at-home orders, potentially leading to country reputation damage. We resort to sentiment analysis to investigate changes in opinions about Italy reported on Twitter before and after the COVID-19 outbreak. Using different lexicons-based methods, we find a breakpoint corresponding to the date of the first established case of COVID-19 in Italy that causes a relevant change in sentiment scores used as a proxy of the country’s reputation. Next, we demonstrate that sentiment scores about Italy are associated with the values of the FTSE-MIB index, the Italian Stock Exchange main index, as they serve as early detection signals of changes in the values of FTSE-MIB. Lastly, we evaluate whether different machine learning classifiers were able to determine the polarity of tweets posted before and after the outbreak with a different level of accuracy.

Suggested Citation

  • Gianpaolo Zammarchi & Francesco Mola & Claudio Conversano, 2023. "Using sentiment analysis to evaluate the impact of the COVID-19 outbreak on Italy’s country reputation and stock market performance," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(3), pages 1001-1022, September.
    • Handle: RePEc:spr:stmapp:v:32:y:2023:i:3:d:10.1007_s10260-023-00690-5
    DOI: 10.1007/s10260-023-00690-5
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    References listed on IDEAS

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    1. Zeileis, Achim & Leisch, Friedrich & Hornik, Kurt & Kleiber, Christian, 2002. "strucchange: An R Package for Testing for Structural Change in Linear Regression Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 7(i02).
    2. Malcolm Baker & Jeffrey Wurgler, 2006. "Investor Sentiment and the Cross‐Section of Stock Returns," Journal of Finance, American Finance Association, vol. 61(4), pages 1645-1680, August.
    3. De Rosis, Sabina & Lopreite, Milena & Puliga, Michelangelo & Vainieri, Milena, 2021. "The early weeks of the Italian Covid-19 outbreak: sentiment insights from a Twitter analysis," Health Policy, Elsevier, vol. 125(8), pages 987-994.
    4. Jushan Bai & Pierre Perron, 1998. "Estimating and Testing Linear Models with Multiple Structural Changes," Econometrica, Econometric Society, vol. 66(1), pages 47-78, January.
    5. Carol Shofiya & Samina Abidi, 2021. "Sentiment Analysis on COVID-19-Related Social Distancing in Canada Using Twitter Data," IJERPH, MDPI, vol. 18(11), pages 1-10, June.
    6. Cynthia Chew & Gunther Eysenbach, 2010. "Pandemics in the Age of Twitter: Content Analysis of Tweets during the 2009 H1N1 Outbreak," PLOS ONE, Public Library of Science, vol. 5(11), pages 1-13, November.
    7. Mohammad Daradkeh, 2022. "Analyzing Sentiments and Diffusion Characteristics of COVID-19 Vaccine Misinformation Topics in Social Media: A Data Analytics Framework," International Journal of Business Analytics (IJBAN), IGI Global, vol. 9(3), pages 1-22, July.
    8. Pfaff, Bernhard, 2008. "VAR, SVAR and SVEC Models: Implementation Within R Package vars," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i04).
    9. Jushan Bai & Pierre Perron, 2003. "Computation and analysis of multiple structural change models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(1), pages 1-22.
    10. David A Broniatowski & Michael J Paul & Mark Dredze, 2013. "National and Local Influenza Surveillance through Twitter: An Analysis of the 2012-2013 Influenza Epidemic," PLOS ONE, Public Library of Science, vol. 8(12), pages 1-1, December.
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