  One of the most important incidents in the world in 2020 is the outbreak of
the Coronavirus. Users on social networks publish a large number of comments
about this event. These comments contain important hidden information of public
opinion regarding this pandemic. In this research, a large number of
Coronavirus-related tweets are considered and analyzed using natural language
processing and information retrieval science. Initially, the location of the
tweets is determined using a dictionary prepared through the Geo-Names
geographic database, which contains detailed and complete information of places
such as city names, streets, and postal codes. Then, using a large dictionary
prepared from the terms of economics, related tweets are extracted and
sentiments corresponded to tweets are analyzed with the help of the RoBERTa
language-based model, which has high accuracy and good performance. Finally,
the frequency chart of tweets related to the economy and their sentiment scores
(positive and negative tweets) is plotted over time for the entire world and
the top 10 economies. From the analysis of the charts, we learn that the reason
for publishing economic tweets is not only the increase in the number of people
infected with the Coronavirus but also imposed restrictions and lockdowns in
countries. The consequences of these restrictions include the loss of millions
of jobs and the economic downturn.
