A systematic study on predicting depression using text analytics

Abstract

Social Networking Sites (SNS) provides online communication among groups but somehow it is affecting the status of mental health. For adolescents with limited social media friends and using internet for communication purposes predicted less depression, whereas  non-communication desire reveals more depression and anxiety disorder. Social media posts and comments provide a rich source of text data for academic research. In this paper, we have discussed various text analytical approaches to predict depression among users through the sharing of online ideas over such websites. This paper presents a  comprehensive review for predicting depression disorder by various text analytics approaches. This paper also presents the summary of results obtained by some researchers available in literature to predict Major
Depressive Disorder (MDD). In future research, enable self-monitoring of health status of each individuals which may help to increase well-being of an identity.

Keywords: Social Networking Sites; Sentiment Analysis; Machine Learning; Support Vector Machine.