Student feedback plays a crucial role in evaluating teaching effectiveness, course quality, and overall academic experience. However, manually analyzing large volumes of textual feedback is time-consuming and prone to bias. With the rapid growth of educational institutions and digital feedback systems, there is a need for an automated and intelligent approach to analyze student opinions efficiently. This project presents a Student Feedback Sentiment Analysis system using Natural Language Processing (NLP) and Machine Learning (ML) techniques to automatically classify feedback into Positive, Negative, and Neutral categories. The proposed system processes raw student feedback text through several stages including text preprocessing, feature extraction using TF-IDF, and sentiment classification using college-safe machine learning algorithms such as Naive Bayes, Logistic Regression, and Support Vector Machine (SVM). The dataset consists of real student feedback collected at the college level. Experimental results show that the proposed approach achieves high accuracy and reliable sentiment classification performance. The system helps educational institutions identify strengths and weaknesses in teaching methodologies, course structure, and infrastructure, enabling data-driven decision-making for academic improvement.
Student Feedback Analysis; Sentiment Analysis; Natural Language Processing; Machine Learning; Text Classification
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