Abstract:online social networks provide relevantinformation 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 emotionalhealth monitoring system to detect users with possible psychological disordersspecially depression and stress. Symptoms of this psychological disorder areusually observed passively. In this situation, author argue that online socialbehaviour extraction offers an opportunity to actively identify psychologicaldisorder at an early stage. It is difficult to identify the disorder becausethe psychological factors considered in standard diagnostic criteriaquestionnaire cannot be observed by the registers of online social activities.Our approach, New and innovative for the practice of psychological disorderdetection, it does so do not trust the self-disclosure of those psychologicalfactors through the questionnaires. Instead, propose a machine learningtechnique that is detection of psychological disorder in social networks whichexploits the features extracted from social network data for identify withprecision possible cases of disorder detection. We perform an analysis of thecharacteristics and we also apply machine learning in large-scale data sets andanalyse features of the two types of psychological disorders.
Keywords-Sentiment analysis,knowledge personalization and customization, detection system, social networks,machine learning