Mental Anxiety and Depression Detection During Pandemic Using Machine Learning

Abstract

As the novel coronavirus pandemic sweeps the globe and people take to their homes to avoid getting and spreading the contagion, it makes proper sense that much of the conversation about this is taking place online. With the rise of Social Media usage, web surfing and a long period of uncertainty during this Pandemic time, there is a sheer concern about the mental health and anxiety disorders among people. People are now using the internet to share information, air their anxieties, and spend time while in quarantine. This increate rate of online Social Media Use (SMU) opened the possibility to identify some common traits among people with various mental disorders and anxiety by the large dataset provided. The moments when those online conversations light up also tell us a lot about how our feelings around the pandemic are evolving. In recent years, this research area has started to evolve, but it would be extremely valuable during this crisis period. Although it is a complex task to perform as mental illness patterns are very complicated, it showed the light of hope in the past. Previously, adoptive supervised machine learning, such as deep neural network approaches were used to predict the pattern and level of mental illness; but they failed due to lack of annotated training data. In this research, we are proposing an effective machine learning architecture, based on Cluster analysis, Natural Language Processing (NLP) technique in the analysis of unstructured data extraction from Social media platforms and couple of psychological screener to classify mental condition of people.