Dual-Track Aspect-Level Sentiment Analysis for Alleviating Cold Start in MOOC Course Reviews

In the realm of contemporary educational data mining, aspect-based sentiment analysis plays a crucial role in deciphering students' nuanced perceptions of MOOC courses. However, sentiment analysis in educational context often encounters the prevalent challenge of cold start issues. This paper proposes a novel methodology for aspect-level sentiment analysis of course reviews, beginning with the identification of critical aspects in course reviews, followed by a comprehensive sentiment analysis at the aspect level. We introduce a Dual-Track Sentiment Analysis model (DTSA), which dynamically integrates two analytical tracks: one utilizing fine-tuned BERT model and the other employing sentiment dictionaries to effectively mitigate the cold start problem. Experimental results demonstrate the superiority of our approach over baseline models in various key metrics, particularly in addressing cold start challenges with limited review data. By incorporating a matching strategy, our model ensures reliable and timely sentiment analysis of course reviews, even with small amount of course reviews. This methodology effectively alleviates the cold start problem in aspect-level sentiment analysis in educational evaluation text, providing accurate insights when lacking sufficient initial learners' review data and enhancing the robustness of MOOC course evaluation processes.