Stress Detection using Vocal Expression & Text Mining to understand Mental Condition

Abstract

Depression is likely to be one of the most important social health issues in today's culture. Suicide was considered by mentally sick or depressed persons. It can be used as a suicide risk indicator. India is one of the countries with the highest annual suicide rate in the world. The goal of Face Emotion Recognition (FER) is to identify a person's emotions in order to lower the suicide rate. The (CNN, 2.17) algorithm is used to extract facial features and assess stress utilising emotions communicated through the face as a threshold. This system is primarily used to categorise good and negative emotions, as well as to identify stress using a standard threshold value. At final we are able to make final choice primarily based on above two techniques. To generate detailed dashboard of user disease status and to design webapp for above system. We will use CNN algorithm for speed up detection of depressed character instances and approach to become aware of high quality answers of mental health troubles. We suggest system learning method as an efficient and scalable technique. We document an implementation of the proposed method. We've evaluated the efficiency of our proposed technique the usage of a set of various psycholinguistic features. We show that our proposed method can extensively improve the accuracy and category blunders price.