MTL-BERTNet: A Multi-Task Learning Framework for Aspect and Sentiment Analysis in MOOC Reviews
TL;DR
MTL-BERTNet is proposed, a novel multi-task learning architecture that jointly performs aspect category classification and sentiment polarity detection from MOOC reviews that leverages contextual embeddings from a pre-trained BERT encoder and integrates a convolutional multi-head attention mechanism to capture subtle semantic nuances and inter-task dependencies.
Abstract
In the context of large-scale online learning environments, analyzing student feedback is crucial for improving course content and learner engagement. This paper proposes MTL-BERTNet, a novel multi-task learning architecture that jointly performs aspect category classification and sentiment polarity detection from MOOC reviews. The model leverages contextual embeddings from a pre-trained BERT encoder and integrates a convolutional multi-head attention mechanism to capture subtle semantic nuances and inter-task dependencies. To further enhance shared representation learning across tasks, an inter-task matching layer (IML) is introduced. Experiments conducted on an imbalanced MOOC review dataset demonstrate strong performance, with macro F1-scores of 0.90 for aspect classification and 0.93 for sentiment prediction. These results highlight the effectiveness of jointly modeling aspects and sentiment, offering practical insights for improving course design, instructional quality, and learner satisfaction in MOOC platforms.