Abstract

One of the most prevalent and disabling mental conditions that seriously influence society is stress and depression. The use of social networking to improve the detection of stress and depression may require automatic health monitoring systems. Sentiment analysis refers to the use of content mining and natural language processing techniques with the goal of identifying feelings or opinions. full of emotion Computing is the study and development of systems and equipment that can recognize, understand, process, and imitate human effects. Deep learning and sentiment analysis approaches could provide effective algorithms and frameworks for a target evaluation and observation of mental issues, particularly depression and stress. This research discusses the use of sentiment analysis and deep learning approaches for the detection and monitoring of stress and depression. Additionally, a fundamental foundation for a multimodal framework that integrates estimating investigation and extensive methods for processing feelings is given. This framework will be used to assess for stress and sadness. The paper specifically traces the fundamental problems and compares them to the framework's structure.