Sentiment Analysis of Student Feedback in Higher Education Using Natural Language Processing (NLP)

Abstract

Student feedback is a vital tool for assessing and improving the quality of education in higher institutions. However, traditional methods of analyzing feedback are time-consuming, subjective, and often fail to provide actionable insights. This research explores the application of Natural Language Processing (NLP) techniques for sentiment analysis of student feedback, aiming to automate the process and derive meaningful patterns from unstructured textual data. The study proposes a comprehensive framework incorporating advanced NLP models such as Support Vector Machines (SVM), Long Short-Term Memory (LSTM) networks, and BERT (Bidirectional Encoder Representations from Transformers) to classify sentiments into positive, negative, and neutral categories. The framework also identifies recurring themes in feedback, offering deeper insights into student satisfaction and areas needing improvement. While the study acknowledges challenges such as handling complex sentiments, linguistic diversity, and data privacy concerns, it provides a robust methodology for data preprocessing, model evaluation, and sentiment interpretation. The results demonstrate the potential of NLP-driven sentiment analysis to enhance decision-making, promote a student-centered approach, and drive continuous improvement in higher education. This research contributes to the groundwork for future studies to explore multilingual models, real-time analysis, and ethical considerations in the use of automated sentiment analysis tools.