Sentiment Analysis based Emotion Extraction for COVID-19 Using Crawled Tweets and Global Statistics for Mental Health

Abstract

The unpredictable and crucial challenges that occurred because of the COVID-19 pandemic disease have taken a gradual upsurge impacting over 213 countries across the globe. Different countries have taken several measures to get control over it like Lockdown, Curfews, Travel ban, etc. but still the cases were increasing and the situation was getting worse globally during some period of time. The impacts on the financial, social, and physical aspects of several citizens resulted in their psychological and mental health issues. In this work, we have quantitatively analyzed the depression, stress, and suicide cases during the period of COVID-19 globally and especially, in India. The global data including tweets (collected using a Scraper) is used for analysis. The data have been analyzed on Tableau and; sentiment analysis for extracting emotions in tweets has been performed using Python. Tweets are analyzed to extract the emotion of people in terms of Fear, Sadness, Anger, and Happiness. With total collected Tweets of 819678 from Jan 2020 to March 2022, it is found that people are more into Fear and Sadness with 59.3% and 28.9% scores respectively.