Design and Implementation of a Smart Feedback System for Lecturers Using Sentiment

Abstract Traditional feedback methods are often manual, time-consuming, and susceptible to subjective interpretation, which limits their effectiveness for timely instructional improvement. To address these challenges, a web-based system was developed to streamline feedback collection and management. The system integrates a Flask-based sentiment analysis engine powered by Natural Language Processing (NLP) techniques to automatically classify student feedback into positive, negative, or neutral categories. The proposed system includes a user-friendly student feedback interface, a secure lecturer dashboard, automated sentiment classification, and graphical visualization of feedback trends using charting tools. These features work together to provide an efficient and structured approach to feedback analysis. System testing confirmed accurate sentiment classification, seamless integration between components, and effective real-time processing of feedback data. The system demonstrates how artificial intelligence can enhance teaching evaluation by providing lecturers with timely, data-driven insights that support continuous improvement in instructional delivery. Keywords: Smart Feedback, Evaluation, Sentiment Analysis, Lecturers, NLP