Depression detection from social media posts using multinomial naive theorem

Abstract

All 350+ million people worldwide are suffering from mental disorder called Depression. An individual who is suffering from depression functions below average in life, is vulnerable to other diseases and in the worst-case, depression leads to suicide. There are many restraints preventing expert care from reaching people suffering from depression in time. Impediments such as social stigma associated with mental disorders, lack of trained health-care professionals and ignorance of the signs of depression owing to a lack of awareness of the disease. The World Health Organization (WHO) suggests that people who are depressed are regularly not correctly diagnosed and others s who are misdiagnosed are prescribed antidepressants. Thus, there is a strong need to automatically assess the risk of depression. Social media platforms increasingly come closer to become a true digitization of the human social experience. In many cases people would in fact prefer to express themselves online than offline. In this paper, we are using Facebook comments as data set, and based on it categorizing the users as depressed or non-depressed.