An Evaluation Framework for Online Courses Based on Sentiment Analysis Using Machine Learning

Online course evaluation is critical to both course selection and teaching effectiveness for students and teachers. However, the current online course evaluation methods have been criticized for neglecting learners’ needs and their inefficiency. Therefore, a course evaluation framework based on sentiment analysis using machine learning is proposed in this study to analyze a large number of online course review comments from learners. Initially, massive open online course review comments were collected through web crawling. Then, sentence- and aspect-based sentiment analyses were performed. Finally, a list of aspect terms that reflected the learners’ requirements was compiled based on the model-generated outcomes. The model was utilized to evaluate an online intellectual property law online course. Results demonstrate that the training models built in this study achieve over 90% accuracy and that 90%–95% of learners are satisfied with the intellectual property law online course. The learners are particularly satisfied with the teacher’s teaching style and course schedule. However, the models also highlight the insufficient interactivity in the class and the scarcity of novel course cases. The proposed framework provides a learner-centric approach to evaluating online courses, thereby enhancing the credibility of online course evaluation. This framework also serves as a practical reference for online course recommendation and construction.