  Understanding the public sentiment and perception in a healthcare crisis is
essential for developing appropriate crisis management techniques. While some
studies have used Twitter data for predictive modelling during COVID-19,
fine-grained sentiment analysis of the opinion of people on social media during
this pandemic has not yet been done. In this study, we perform an in-depth,
fine-grained sentiment analysis of tweets in COVID-19. For this purpose, we
perform supervised training of four transformer language models on the
downstream task of multi-label classification of tweets into seven tone
classes: [confident, anger, fear, joy, sadness, analytical, tentative]. We
achieve a LRAP (Label Ranking Average Precision) score of 0.9267 through
RoBERTa. This trained transformer model is able to correctly predict, with high
accuracy, the tone of a tweet. We then leverage this model for predicting tones
for 200,000 tweets on COVID-19. We then perform a country-wise analysis of the
tone of tweets, and extract useful indicators of the psychological condition
about the people in this pandemic.
