Mental Health Analysis and Classification During Covid-19 Using Big Data Approach

In December 2019, a deadly virus named SARS-CoV-2 started spreading in the regions of Wuhan, Hubei, China. The number of coronavirus patients gradually increased in Wuhan, and by 20 December, it reached 60 and 266 by 31 December. Till now, there have been more than 40 Lakhs deaths due to Covid-19. This deadly pandemic gave a setback to most people all over the world in terms of losing their loved ones. Apart from that, this pandemic mentally affected a lot of minds. Social illness and loneliness have been linked to poor mental health by a broad body of research, and data from late March suggests a negative increase in mental health. There had been news of people committing suicides or some going under depression all because their social life was cut down and all they did was question their life choices, their existence, their personality, and their achievements which ultimately trapped them in those intrusive thoughts that kept popping up again and again—which made them disturbed or even distressed. The objective of this paper is to analyze and categorize the mental states of people from all over the world in order to raise mental health awareness, particularly during COVID-19. We used the big data approach to display the surge in sadness and suicidal ideation in terms of the increase in the frequency of certain words. To continue with this problem statement, we will examine text data and learn what words are utilized in virtual suicide/depression notes utilizing two subreddits and NLP tools.