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AN ENHANCED MULTIPLE CLASSIFICATION ON SOCIAL NETWORK MENTAL DISORDERS DETECTION (SNMDD) AND BLOCK THE USER

  • Yoga M Associate Professor, Department of Information Technology , Nandha College of Technology, Erode-638052, Tamilnadu, India.
  • Akash Kumar N.S UG scholar, Department of Information Technology , Nandha College of Technology, Erode-638052, Tamilnadu, India
  • Biddu Mohan UG scholar, Department of Information Technology , Nandha College of Technology, Erode-638052, Tamilnadu, India
  • Gowri sankar B UG scholar, Department of Information Technology , Nandha College of Technology, Erode-638052, Tamilnadu, India
  • NaveenKumar N UG scholar, Department of Information Technology , Nandha College of Technology, Erode-638052, Tamilnadu, India

Abstract

The rates of diagnosing depression and mental illness during the last few decapods, a number of cases prevail unheard-of. Symptoms linked to mental illness are detectable on Twitter, Facebook and web forums and automatic methods are more and more able to locate inactivity and other mental disease. In this paper, latest studies that planned to detect depression and mental illness by the use of social media are surveyed. Mentally ill users have already been pointed out the use of screening surveys, their community distribution of analysis on twitter, or by their membership in online forums, and that they were detectable originating at regulate users by patterns in their language and online activity. Various automated detection methods can help to detect depressed people using social media.

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