Real Time Machine Learning Based Emotion Detection for Retail Stores to Capture Customer’s Feedback Based on Their Emotion

This project investigates the application of real-time machine learning for emotion detection aimed at enhancing customer feedback collection in retail environments. As 5G and 4D technologies become increasingly main stream, there's a growing demand for smart advertising methods that are both cost-effective and rapid in delivery. Emotion recognition in realtime has surfaced as a viable approach to gather customer insights by interpreting their emotional responses. The system put forward integrates computer vision and NLP (Natural Language Processing) to monitor and interpret customer emotions through their facial cues, gestures, and spoken interactions in real-time. By adopting this solution, businesses can gain immediate emotional feedback and adjust their services or product offerings accordingly to better suit customer expectations. The main goal of this study is to architect and implement a real-time emotion detection framework capable of reliably identifying and categorizing shopper emotions inside retail setups. To realize this, the research delves into a variety of machine learning techniques and vision-based methods focused on decoding body movements and facial expressions. It further includes a discussion on NLP strategies that facilitate the evaluation of spoken or written customer input.