• CHINMAY M. DEO
• DR.PROF.S.B.SONKAMBLE

online social networks provide relevant information on users' opinions and posts on various topics. So applications, such as monitoring and detection systems can collect and analyse this data. This paper presents a knowledge-based system, which includes an emotional health monitoring system to detect users with possible psychological disorders specially depression and stress. Symptoms Of these psychological disorder are usually observed passively. In this situation, author argue that online social behaviour extraction offers an opportunity to actively identify psychological 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 psychological disorder detection, it does so do not trust the self-disclosure of those psychological factors through the questionnaires. Instead, propose a machine learning technique that is detection of psychological disorder 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 in large-scale data sets and analyse features of the two types of psychological disorders.

Sentiment analysis, knowledge personalization and customization, detection system, social networks, machine learning.

"A Survey on Machine Learning Based Sentimental Analysis Knowledge Framework", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 6, page no.1342-1346, June-2020, Available :http://www.jetir.org/papers/JETIR2006196.pdf

"A Survey on Machine Learning Based Sentimental Analysis Knowledge Framework", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 6, page no. pp1342-1346, June-2020, Available at : http://www.jetir.org/papers/JETIR2006196.pdf