  As the Covid-19 outbreaks rapidly all over the world day by day and also
affects the lives of million, a number of countries declared complete lock-down
to check its intensity. During this lockdown period, social media plat-forms
have played an important role to spread information about this pandemic across
the world, as people used to express their feelings through the social
networks. Considering this catastrophic situation, we developed an experimental
approach to analyze the reactions of people on Twitter taking into ac-count the
popular words either directly or indirectly based on this pandemic. This paper
represents the sentiment analysis on collected large number of tweets on
Coronavirus or Covid-19. At first, we analyze the trend of public sentiment on
the topics related to Covid-19 epidemic using an evolutionary classification
followed by the n-gram analysis. Then we calculated the sentiment ratings on
collected tweet based on their class. Finally, we trained the long-short term
network using two types of rated tweets to predict sentiment on Covid-19 data
and obtained an overall accuracy of 84.46%.
