A Review on Depression and Stress monitoring System via Social Media Data using Deep learning Framework

Abstract

Stress and depression are prevalent mental health conditions that significantly impact society. The use of automated health monitoring systems can be vital in improving the detection and management of depression and stress through social networking. Sentiment analysis involves natural language processing and text mining techniques that aim to identify emotions and opinions. Emotional computing is the development and study of devices and systems that can recognize, interpret, process, and mimic human emotions. By using sentiment analysis and deep learning techniques, effective algorithms and systems can be created to target the assessment and monitoring of mental health disorders, especially depression and stress. This paper discusses the application of sentiment analysis and deep learning methods in detecting and monitoring depression and stress. Additionally, the paper proposes a basic design for an integrated multimodal system for stress and depression monitoring that incorporates sentiment analysis and emotional processing techniques. Specifically, the paper outlines the key issues and challenges involved in developing such a system.