Hybrid Emotion Classification of MOOC Reviews Using the NRC Lexicon and a Multi-Channel Deep Learning Model

Abstract

Text-based emotion recognition has received extensive attention in applied computing research, but its effectiveness in online learning contexts remains limited. In this study, we introduce the TriFusion Attention Network, a hybrid deep learning model that classies emotions in Massive Open Online Course (MOOC) reviews. Using the NRC Emotion Lexicon, we annotated learner reviews and designed the model to integrate multiple channels capturing both semantic and affective information. Its architecture combines Bidirectional Long Short-Term Memory (BiLSTM), Bidirectional Gated Recurrent Units (BiGRU), Convolutional Neural Networks (CNN), and attention mechanisms to model the complexity of learner feedback effectively. Experiments conducted on Coursera reviews demonstrate that the model effectively identifies both explicit and subtle emotional cues, achieving over 95% accuracy, F1-scores around 0.95, and AUC-ROC values approaching 0.99 on both balanced and imbalanced datasets. These results confirm that the proposed approach achieves superior performance compared to existing methods and facilitates improved learner engagement while offering richer analytical insights into their experiences.