• Miss. Shilpa M. Kedari
• Prof. Pooja Patil

Now a days popularity of social networking sites Leads to the problematic usage. An increasing number of psychological mental disorders in social networks, dependence on cybernetic relationships, information overload, and Net Compulsion have been reported recently. Symptoms Of these psychological disorders are usually observed passively. In this situation, author argue that online social behaviour extraction offers an opportunity to actively identify disorder at an early stage. It is difficult to identify the disorder because the psychological factors considered in standard diagnostic criteria questionnaire cannot be observed by the registers of online social activities. Our approach, New and innovative for the practice of disorder detection, it does so do not trust the self-disclosure of those psychological factors through the questionnaires. Instead, propose a machine learning approach That is detection of psychological disorders in social networks Which exploits the features extracted from social network data For identify with precision possible cases of disorder detection. We perform an analysis of the characteristics and we also apply machine learning classifier in large-scale data sets and analyse features of the three types of psychological mental disorders.

Online social networking sites (OSN), Psychological mental disorder detection, feature extraction, SNMD Classifier.

"Mental Disorders Detection via Online Social Media Mining using Machine Learning ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 7, page no.744-748, July-2020, Available :http://www.jetir.org/papers/JETIR2007090.pdf

"Mental Disorders Detection via Online Social Media Mining using Machine Learning ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 7, page no. pp744-748, July-2020, Available at : http://www.jetir.org/papers/JETIR2007090.pdf