Emotion Analysis from Customer Shopping Experience using ML

Understanding how customers feel is one of the most valuable insights a business can gain. Traditionally, companies depend on surveys, ratings, or written feedbacks to collect reviews—but these methods are often time-consuming, biased, or ignored by users. With the rapid advancement of Artificial Intelligence (AI) and Machine Learning (ML), it is now possible to automatically detect and interpret human emotions using technologies like facial recognition, video analysis, and Natural Language Processing (NLP). This paper presents a comprehensive survey of the latest research efforts aimed at improving customer feedback systems using AI-powered emotion recognition. Many researchers try to understand emotions such as happiness, sadness, anger, or curiosity by studying people’s facial expressions, often using images or videos taken from cameras or webcams. Popular techniques include Convolutional Neural Networks (CNNs) for image-based emotion classification, Support Vector Machines (SVMs) for quick categorization, and Recurrent Neural Networks (RNNs) like GRU and L.This document gives formatting instructions for authors preparing papers for publication in the Proceedings of an IEEE conference. The authors must follow the instructions given in the document for the papers to be published. You can use this document as both an instruction set and as a template into which you can type your own text.