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 frst European countries to be severely afected 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
diferent lexicons-based methods, we fnd a breakpoint corresponding to the date of
the frst 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 diferent machine
learning classifers were able to determine the polarity of tweets posted before and
after the outbreak with a diferent level of accuracy
