ABSTRACT
The world has come to a standstill due to the Covid-19 Pandemic. Managing mental and psychological health remains as important as physical health in such situations. With work from home becoming the new normal, social media has been an integral part of sharing user sentiments through tweets during these tough times which can later be used to interpret the emotions behind those tweets. Our system of study aims to perform emotion analysis by fetching user data from twitter, and analysing those tweets to understand user sentiments over a period of time. This is achieved by using Facebook’s Fasttext for text classification which classifies tweets into emotions namely Anger, Relief, Boredom, Happiness, Hate, Fun, Love, Surprise, Worry, Enthusiasm, Sadness and Empty in the most fastest and efficient way as compared to other algorithms. FastText is an NLP Library generally used for text representations and classifications. The classified sentiments over a period of time gives better understanding of people’s mental health and how the sentiments have changed overtime. © 2021 IEEE.