Open AccessA Study on Sentiment Analysis of Mental Illness Using Machine Learning Techniques
Author(s) -
Pradeep Kumar Tiwari,
M. M. Sharma,
Payal Garg,
Tarun Jain,
Vivek Kumar Verma,
Afzal Hussain
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1099/1/012043
Subject(s) - social media , depression (economics) , mental illness , sentiment analysis , psychology , anxiety , internet privacy , mental health , psychiatry , computer science , world wide web , artificial intelligence , economics , macroeconomics
In the digital age, social media plays a crucial role in society. Social media provides a platform to youth for exchanging their views on public issues and express their personal issues. Hence online media can be used for studying the behavior of people. Applying sentiment analysis on the data obtained timely from social networking sites (here Twitter), depression, anorexia, and other similar mental illness can be predicted among youth. The importance of detecting depression is that it is the root cause of a plethora of diseases. Early prediction can also mitigate the number of suicides. This work is to detect depression and PTSD (Post Traumatic Stress Disorder) among the Twitter users. Analysing the tweets, how likely a person is to suffer from any of the aforementioned diseases can be discovered.