Course Review Sentiment Analysis: A Comparative Study Of Machine Learning and Deep Learning Methods

In this ever-evolving world of education, one of the key tools for improving the quality of teaching and learning is understanding student sentiment and feedback through course reviews. This study analyzed more than 100,000 reviews of courses on the online learning platform Coursera, with the objective of comparing the efficacy of various sentiment analysis methods. The initial preprocessing steps involved data cleaning using different Natural Language Processing (NLP) techniques. The cleaned data subsequently underwent transformation into TF-IDF and word embeddings were created using a pre-trained model. Naive Bayes, Random Forest, and SVM models were trained on the vectorized TF-IDF data and fine-tuned using a grid search method. Meanwhile, the word embeddings were used to train the LSTM and GRU models, which were optimized through Bayesian methods. Additionally, the BERT model was incorporated for further comparison. The findings revealed that BERT outperformed all other models in metrics such as accuracy, precision, recall, and F1 score, making it the most effective for the sentiment analysis of course reviews in this study. This comprehensive analysis aims to offer valuable insight into the sentiments of course evaluation, ultimately striving to improve the educational experiences of students. It also discusses the significance of the findings and highlights potential areas of improvement for future research.