Open AccessDetection of Depression among Social Media Users with Machine Learning
Author(s) -
Senthil Raja M,
L. Arun Raj,
Ankit Arun
Publication year - 2022
Publication title -
webology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.259
H-Index - 18
ISSN - 1735-188X
DOI - 10.14704/web/v19i1/web19019
Subject(s) - popularity , taboo , social media , conversation , mental health , internet privacy , depression (economics) , affect (linguistics) , psychology , natural (archaeology) , computer science , data science , world wide web , psychiatry , social psychology , sociology , communication , history , archaeology , anthropology , economics , macroeconomics
Mental illnesses are a significant and growing public health concern. They have the potential to tremendously affect a person’s life. Depression, in particular, is one of the major reasons for suicide. In recent times, the popularity of social media websites has burgeoned as they are platforms that facilitate discussion and free-flowing conversation about a plethora of topics. Information and dialogue about subjects like mental health, which are still considered as a taboo in various cultures, are becoming more and more accessible. The objective of this paper is to review and comprehensively compare various previously employed Natural Language Processing techniques for the purpose of classification of social media text posts as those written by depressed individuals. Furthermore, pros, cons, and evaluation metrics of these techniques, along with the challenges faced and future directions in this area of research are also summarized.