Detection of Mental Illness In Social Media

Abstract

In the contemporary world filled with technological advancements, early detection of depression is becoming increasingly crucial for timely intervention and effective mental health care. This final year project focuses on developing an innovative Depression Detection system that integrates text and video modalities, leveraging state-of-the-art technologies such as Computer Vision, Real-Time Communication, and Natural Language Processing. The system employs Haar Cascade for real-time facial expression recognition and Python for backend programming, coupled with MySQL for database management. Various APIs are used for seamless data communication, creating a comprehensive and intuitive environment for depression detection. The interface, guided by user inputs, tracks and analyzes both textual and visual data to identify signs of depression. The system then automates alerts and personalized recommendations, sent via communication channels to facilitate timely intervention. This project reflects a dedication to pushing the boundaries of technological innovation in mental health care. It addresses the critical need for advanced systems that provide a seamless and efficient experience in detecting and managing depression. The challenges encountered during the development process have been instrumental in enhancing both the technical aspects of the system and my personal and professional growth. The report details the design, implementation, and evaluation of this Depression Detection system. It highlights the iterative development process, the integration of cutting-edge technologies, and the significant potential for improving user engagement and detection precision. This project aims to inspire future researchers and technologists to explore the vast possibilities in the intersection of mental health and technology, ultimately contributing to better mental health care outcomes