  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 proxy of the country reputation. Next, we demonstrate
that sentiment scores about Italy are strongly associated with the levels 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. Finally, we make
a content-based classification of tweets into positive and negative and use two
machine learning classifiers to validate the assigned polarity of tweets posted
before and after the outbreak.
