Sentiment analysis of student feedback using machine learning and lexicon based approaches

Abstract

This paper presents a combination of machine learning and lexicon-based approaches for sentiment analysis of students feedback. The textual feedback, typically collected towards the end of a semester, provides useful insights into the overall teaching quality and suggests valuable ways for improving teaching methodology. The paper describes a sentiment analysis model trained using TF-IDF and lexicon-based features to analyze the sentiments expressed by students in their textual feedback. A comparative analysis is also conducted between the proposed model and other methods of sentiment analysis. The experimental results suggest that the proposed model performs better than other methods.