Enhanced Bidirectional LSTM for Sentiment Analysis of Learners’ Posts in MOOCs

Abstract

Massive Open Online Courses (MOOCs) have transformed digital learning, leading to vast amounts of learner-generated content that reflect user experience and engagement. Accurately classifying sentiment from this content is essential for improving course quality, but remains challenging due to subtle linguistic variation and contextual ambiguity. This study proposes a sentiment analysis approach based on an enhanced Bidirectional Long Short-Term Memory (LSTM) model. The enhancements include the integration of data augmentation and regularization techniques to address overfitting and improve generalization. The model was trained and evaluated on a dataset of 29,604 learner discussion posts from Stanford University MOOCs. Experimental results show that the proposed model achieves an accuracy of 88.54% in classifying sentiments into positive, negative, and neutral classes. These results suggest that the enhanced LSTM model offers a reliable solution for large-scale sentiment classification in online education, with potential applications in learner support, curriculum design, and personalized feedback.