Depression Detection Using Chatbot and Live Video Facial Analysis

Depression significantly affects millions of individuals worldwide, influencing mental, physical, and emotional well-being. Early detection and timely intervention are crucial to improve the quality of life for individuals living with depression. This study introduces an innovative AI-driven system that integrates chatbot technology, facial expression analysis via live video, and machine learning algorithms for detecting depression. The system utilizes a unique combination of real-time facial analysis and chatbot interactions to assess users’ emotional states, capturing subtle patterns indicative of depression. By analyzing facial expressions and engaging users in conversations, the AI-driven system provides an empathetic space for individuals to express their feelings. The underlying machine learning model is trained on a specialized dataset, which includes depression-related tweets extracted from Twitter to analyze text-based sentiments and emotions. Various supervised machine learning algorithms, including support vector machines (SVM) and deep learning neural networks, are employed to predict depressive symptoms accurately. The system’s performance is evaluated through cross-validation, ensuring optimal accuracy. The results demonstrate that the proposed AI tool enhances mental health diagnostics by offering a real-time, accessible, and non-invasive platform for early depression detection, complementing traditional assessment methods.