Sentiment Analysis and Topic Mining Using a Novel Deep Attention-Based Parallel Dual-Channel Model for Online Course Reviews

The sentiment analysis and topic mining of course reviews are helpful for course improvement and development. In order to improve the quality of online teaching and effectively mine the information such as sentiments contained in course reviews, a novel Deep Attention-based Parallel Dual-Channel Model (DAPDM) is proposed by combining deep learning neural network algorithms. Bidirectional Encoder Representation from Transformers (BERT) is used to train word vectors. Convolutional neural network (CNN) and bi-directional long short-term memory (BiLSTM) with attention mechanism are used to form a dual-channel model to extract sentiment features and enrich semantics. Firstly, a total of 48,501 online course reviews are selected for experiment and analysis. BERT is also used for data enhancement to obtain balanced data. And the data are substituted into DAPDM and 8 other comparative models to verify the model performance. Secondly, the student-course-institution tripartite graph relationship network and the different sentiment feature words co-occurrence network are constructed and visualized to further study the internal relationship among students, courses, and institutions. Finally, the latent dirichlet allocation (LDA) model is used to extract concerns of different sentiments. The classification accuracy, the macro-average of F1 and the weighted average of F1 on DAPDM are respectively improved to 89.44%, 0.8195, and 0.8939 compared with the comparison model. And its receiver operating characteristic (ROC) curve results are optimal. The relationship network can uncover the most popular courses and institutions, and discover that courses serve as a bridge between students and institutions. It is also found that learners’ reviews mainly focus on the course content, technical content, difficulty degree, teachers’ teaching level, etc., which are also the main factors affecting the course learners’ satisfaction with the course. The study can provide theoretical and technical support for the specification and development of online courses.