Depression Detection Using Deep Learning

Depression is a widespread illness that has potentially devastating consequences. With the rapid growth of social media and internet users, timely identification of emotional responses has become critical. Mental diseases are extremely dangerous, affecting about 300 million people. Therefore, this is why research is being conducted on the subject. With advances in machine learning and the availability of sample data related to depression, there is the prospect of building an early depression diagnostic system, which is critical to reducing the number of people suffering from depression. In this paper, the application of sentiment analysis and deep learning methodologies to depression and stress detection and monitoring are discussed. In addition, a fundamental plan of an incorporated multimodal framework for stress and depression checking, that incorporates estimation investigation and deep learning processing strategies, is proposed. In particular, the paper traces the fundamental issues and moves comparative with the structure of such a framework.