DEVELOPMENT OF A SENTIMENT ANALYSIS SYSTEM USING COMPUTER VISION

This project presents a facial emotion recognition system designed to detect and classify human emotions in real-time using computer vision and deep learning techniques. The system utilizes the DeepFace library to perform facial image preprocessing, face detection, alignment, and emotion classification from either webcam input or uploaded images. The goal is to interpret human emotional states such as happiness, sadness, anger, surprise, fear, and neutrality—by analyzing facial expressions. The backend of the application, developed in Python using the Flask framework, handles image processing, model inference, and result generation. DeepFace simplifies the implementation by integrating multiple emotion recognition models and pre-trained facial analysis capabilities. The frontend, built with React.js, allows users to interactively upload images or capture photos, view detected emotions, and receive visual feedback in an intuitive interface. This system demonstrates the effectiveness of deep learning in understanding non-verbal human cues and has potential applications in education, therapy, user experience testing, and emotion- aware systems. Its modular architecture ensures scalability and ease of integration into larger AI- driven platforms.