  Due to the nature of the data and public interaction, twitter is becoming
more and more useful to understand and model various events. The goal of
CoronaVis is to use tweets as the information shared by the people to visualize
topic modeling, study subjectivity, and to model the human emotions during the
COVID-19 pandemic. The main objective is to explore the psychology and behavior
of the societies at large which can assist in managing the economic and social
crisis during the ongoing pandemic as well as the after-effects of it. The
novel coronavirus (COVID-19) pandemic forced people to stay at home to reduce
the spread of the virus by maintaining social distancing. However, social media
is keeping people connected both locally and globally. People are sharing
information (e.g. personal opinions, some facts, news, status, etc.) on social
media platforms which can be helpful to understand the various public behavior
such as emotions, sentiments, and mobility during the ongoing pandemic. In this
work, we develop a live application to observe the tweets on COVID-19 generated
from the USA. In this paper, we have generated various data analytics over a
period of time to study the changes in topics, subjectivity, and human
emotions. We also share a cleaned and processed dataset named CoronaVis Twitter
dataset (focused on the United States) available to the research community at
https://github.com/mykabir/COVID19. This will enable the community to find more
useful insights and create different applications and models to fight with
COVID-19 pandemic and future pandemics as well.
