• Snehal Nibe
• Ojaswi Borate
• Sayali Manikwar
• Shivani Gund
• Prof. M.A.Rane

A rising amount of Social Network Mental Disorders (SNMDs) are newly noted. One of the key solutions to this problem is detailed study of individual’s behavior attributes. These behavioral attributes can be extracted from various social networking sites such as Twitter, Facebook, etc. Social networking platform is best medium to know person’s behavior, thinking pattern, mood, opinions etc. The individuals on social media express their feelings, daily activities and opinions regarding various topics. So, social networking sites are used as screening tool to predict mental disorder stages. People opinions could be positive, negative or neutral. In order to determine mental disorder stages, person’s negative response is significant because it tells us about the negativism. Earlier method of diagnosis of patient through just psychological questionnaires is not so relevant but by using user generated content on social media definitely helps to predict stages of SNMD of particular individual. Our project aim is to extract information from social media posts and by having clear understanding of person’s behavioral attributes and attempted questionnaires, mental disorder stages of user can be detected. By using machine learning algorithm such as Naive Bayes, mental health status of a user is classified into three SNMD stages like stage 1, stage2, stage3. The results manifest that Social Network Mental Disorder Detection (SNMDD) is capable for identifying and distinguish online social network users with potential SNMDs from user not having mental disorder.

Naïve Bayes Classifier, Social Network Activities, Text Mining, Anomalies, UGC (User generated Content)

"Psychological Illnesses Revealing Method Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 5, page no.48-52, May-2020, Available :http://www.jetir.org/papers/JETIR2005310.pdf

"Psychological Illnesses Revealing Method Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 5, page no. pp48-52, May-2020, Available at : http://www.jetir.org/papers/JETIR2005310.pdf