  Since the first alert launched by the World Health Organization (5 January,
2020), COVID-19 has been spreading out to over 180 countries and territories.
As of June 18, 2020, in total, there are now over 8,400,000 cases and over
450,000 related deaths. This causes massive losses in the economy and jobs
globally and confining about 58% of the global population. In this paper, we
introduce SenWave, a novel sentimental analysis work using 105+ million
collected tweets and Weibo messages to evaluate the global rise and falls of
sentiments during the COVID-19 pandemic. To make a fine-grained analysis on the
feeling when we face this global health crisis, we annotate 10K tweets in
English and 10K tweets in Arabic in 10 categories, including optimistic,
thankful, empathetic, pessimistic, anxious, sad, annoyed, denial, official
report, and joking. We then utilize an integrated transformer framework, called
simpletransformer, to conduct multi-label sentimental classification by
fine-tuning the pre-trained language model on the labeled data. Meanwhile, in
order for a more complete analysis, we also translate the annotated English
tweets into different languages (Spanish, Italian, and French) to generated
training data for building sentiment analysis models for these languages.
SenWave thus reveals the sentiment of global conversation in six different
languages on COVID-19 (covering English, Spanish, French, Italian, Arabic and
Chinese), followed the spread of the epidemic. The conversation showed a
remarkably similar pattern of rapid rise and slow decline over time across all
nations, as well as on special topics like the herd immunity strategies, to
which the global conversation reacts strongly negatively. Overall, SenWave
shows that optimistic and positive sentiments increased over time, foretelling
a desire to seek, together, a reset for an improved COVID-19 world.
