  In 2020, COVID-19 became the chief concern of the world and is still
reflected widely in all social networks. Each day, users post millions of
tweets and comments on this subject, which contain significant implicit
information about the public opinion. In this regard, a dataset of
COVID-related tweets in English language is collected, which consists of more
than two million tweets from March 23 to June 23 of 2020 to extract the
feelings of the people in various countries in the early stages of this
outbreak. To this end, first, we use a lexicon-based approach in conjunction
with the GeoNames geographic database to label the tweets with their locations.
Next, a method based on the recently introduced and widely cited RoBERTa model
is proposed to analyze their sentimental content. After that, the trend graphs
of the frequency of tweets as well as sentiments are produced for the world and
the nations that were more engaged with COVID-19. Graph analysis shows that the
frequency graphs of the tweets for the majority of nations are significantly
correlated with the official statistics of the daily afflicted in them.
Moreover, several implicit knowledge is extracted and discussed.
