Aspect-Based Sentiment Analysis of Student Reviews on MOOC Platforms

The rapid growth of Massive Open Online Courses (MOOCs), particularly in the post-COVID-19 era, has transformed education and led to a substantial increase in student-generated reviews. However, manual analysis of these reviews is impractical due to their sheer volume, and traditional binary sentiment analysis lacks the granularity needed to extract actionable insights. This study proposes a novel framework leveraging Aspect-Based Sentiment Analysis (ABSA) to classify MOOC reviews into seven critical aspects. The model was trained on a 100,000-review dataset from Coursera and a manually annotated 10,000-review dataset from Udemy. Various machine learning techniques were evaluated, with the final model integrating Long Short-Term Memory (LSTM) networks and Support Vector Machines (SVM). The SVM achieved notable performance, with accuracy rates of 93.7% for sentiment prediction and 88.64% for aspect classification on the Coursera dataset, and 73.91% for sentiment prediction and 66.95% for aspect classification on the Udemy dataset. These results underscore the model's efficacy in delivering a comprehensive and platform-independent approach to analyzing MOOC reviews, providing valuable insights for educators and course developers.