  The Coronavirus pandemic has affected the normal course of life. People
around the world have taken to social media to express their opinions and
general emotions regarding this phenomenon that has taken over the world by
storm. The social networking site, Twitter showed an unprecedented increase in
tweets related to the novel Coronavirus in a very short span of time. This
paper presents the global sentiment analysis of tweets related to Coronavirus
and how the sentiment of people in different countries has changed over time.
Furthermore, to determine the impact of Coronavirus on daily aspects of life,
tweets related to Work From Home (WFH) and Online Learning were scraped and the
change in sentiment over time was observed. In addition, various Machine
Learning models such as Long Short Term Memory (LSTM) and Artificial Neural
Networks (ANN) were implemented for sentiment classification and their
accuracies were determined. Exploratory data analysis was also performed for a
dataset providing information about the number of confirmed cases on a per-day
basis in a few of the worst-hit countries to provide a comparison between the
change in sentiment with the change in cases since the start of this pandemic
till June 2020.
