Along with the Coronavirus pandemic, another crisis has manifested itself in the form of mass fear and panic phenomena, fuelled
by incomplete and often inaccurate information. There is therefore a tremendous need to address and better understand
COVID-19’s informational crisis. The diverse use of social networking sites, like Twitter, speeds up the process of sharing
information and having views on community events and health crises COVID-19 has been one of Twitter's trending areas. The
Twitter messages created via Twitter are named Tweets.
In this paper, we identify public sentiment associated with the pandemic using Coronavirus-specific Tweets and Python,
along with its sentiment analysis packages. We provide an overview of two essential machine learning classification methods, in
the context of textual analytics, and compare their effectiveness in classifying Coronavirus Tweets of varying lengths. This
research provides insights into Coronavirus fear sentiment progression, associated methods, limitations, and different
opportunities. In this project, we have designed a Sentiment analysis System that would identify the sentiment of a tweet and
classify it into one of the five classes they include:”ExtremelyPositive”,“Positive”,”Neutral”, ”Negative” and “Extremely
Negative”.