Student Sentiment Analysis Using Various Machine Learning Techniques

Abstract

In terms of social, psychological, physical, technological, and other elements, the educational system is undergoing significant transformation. Today, education is becoming a joint venture between the state, the market, and the community. Alternative education and training providers that place a greater emphasis on employability provide a problem, and university professors represent a particular breed of career academics that remain cut off from developments in the outside world. The sentiment analysis of student comments is presented in this work using a combination of Methodologies based on lexicons and machine learning. The textual feedback, which is often gathered around the conclusion of a semester, offers helpful insights into the general quality of teaching and makes insightful recommendations for ways to enhance instructional design. The article describes a sentiment analysis model trained using TF-IDF and linguistic characteristics to look at the opinions expressed by participants in their textual feedback. Additionally, a comparison among the existing sentiment analysis techniques is done.