Detecting Social Network Addicts Mental Stress Using Machine Learning

Now days social addicts are increasing because usage of internet and smart devices increasing sequentially. In this current situation children to adult are demanded to use smart phones therefore continuous monitoring of users are not possible which leads to privacy issues. In this paper, without disturbing user privacy issue monitoring their chats and analyzing their mental pressure is performed using machine learning. Initially chat text will be extracted and analyzed using natural language processing. By NLP we can process sentimental analysis and identify whether the particular user is in happy or in depression state. By fixing a threshold limit we can detect if the person is in continuous depression or normal mind upset. If our system identifies that the particular person is continuously in depression it will send an intimation to respective care taker mail ID which was collected during registration process of our system. Hence our system detects status of particular person in accurate way through machine learning and take proper step for securing that person from depression.