Exploring Deep Learning Techniques for Sentiment Analysis inOnline Education Platforms: A Case Study of Coursera Reviews

Abstract

The rise of online courses, accelerated by the COVID-19 pandemic, has underscored the need for effective educational models capable of addressing the challenges posed by remote learning. This study focuses on the development of sentiment classifiers using the Coursera reviews dataset to evaluate the polarity of student feedback. This research improved student engagement and support in online education by applying sophisticated sentiment analysis techniques. We explored a comprehensive methodology encompassing various pre-processing techniques, advanced tokenisation methods, and a range of deep learning architectures, including Feedforward Neural Networks (FNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, Gated Recurrent Units (GRUs), and Bidirectional Encoder Representations from Transformers (BERT)-based models. Each model’s performance is optimised through meticulous hyperparameter tuning using the Optuna framework. Results indicated that BERT is the best model, achieving a recall of 97.50% and an accuracy of 96.83%, while Bidirectional LSTM (BiLSTM) closely followed with a recall of 96.55% and an accuracy of 96.71%. In contrast, simpler models like FNN and RNN exhibited lower accuracy (92.83% and 87.83%, respectively). These findings underscore the importance of advanced models in capturing contextual meanings and highlight the effectiveness of leveraging embeddings, attention mechanisms, and tailored pre-processing strategies, which significantly improve sentiment classification performance.