Big Data-Driven Sentiment Analysis Model for Chinese MOOC Reviews

With the rapid development of Chinese MOOCs, user reviews have become an important data source for evaluating course quality and optimizing platform construction. This paper proposes a sentiment analysis model for Chinese MOOC reviews based on big data technology, using aspect-level sentiment analysis methods to explore sentiment tendencies and potential themes in the reviews. Experimental results show that the model has high accuracy and practicality in course quality evaluation and platform optimization. The study indicates that sentiment analysis of learner reviews can provide valuable improvement suggestions for course designers and offer new perspectives and methods for developing MOOC platforms. This paper innovatively introduces big data technology and aspect-level sentiment analysis, filling the research gap in Chinese MOOC sentiment analysis, and providing strong support for improving course quality and learning experience.