Automated Sentiment and Emotion Analysis of Client Feedback

Abstract

This research contributes to automate the analysis of customer feedback with the help of sophisticated machine learning methods like tokenization, sentiment analysis, emotion recognition, and text classification to gain significant insights from answers. Instead of classifying feedback into rigid categories such as compliments or complaints, the system seeks to recognize repeating patterns and emotional tints to provide thorough analysis at the submission stage. It produces graphical reports that facilitate swift data-based decision-making, minimizing manual work and maximizing operational effectiveness. Automated processes enable the organization to respond swiftly to feedback, resulting in ongoing real-time improvement and improved customer satisfaction. The system is designed to scale efficiently with increasing user data, ensuring consistent performance. It also enhances transparency by offering clear visual insights that help stakeholders understand customer needs better. Ultimately, it empowers organizations to refine their services and strengthen customer relationships.