## 2 Related Work
### MOOC Review Text Mining  
As the accumulation of learner reviews continues to grow on MOOC platforms and text mining technology becomes more sophisticated, there has been a surge in research exploring the analysis of MOOC review texts. [PERSON] et al. [30] proposed a weakly supervised aspect-based sentiment analysis method for MOOC reviews, which can effectively reduce the dependence of the model on the number of training samples. [PERSON] et al. [31] proposed a sentiment analysis method that combines Bayesian neural networks, convolutional neural networks (CNNs), and long short-term memory (LSTM). This method demonstrates promising results in the sentiment analysis of MOOC comments. [PERSON] et al. [32] employed the ALBERT-BiLSTM model for sentiment analysis of MOOC reviews, revealing superior performance compared to existing methodologies. To enable data-driven design automation, [PERSON] et al. [33] proposed a text-mining-based approach that automatically extracts feature sentiment pairs from MOOC reviews. In order to improve the performance of sentiment analysis, [PERSON] et al. [34] combined BERT and part-of-speech information in MOOC reviews of sentiment. [PERSON] et al. [35] proposed an MOOC-BERT model to automatically identify learners' cognition from online reviews, which has been verified to be superior to representative deep learning models in recognition and cross-curriculum. [PERSON] et al. [36] categorized learners in MOOCs into beginner, experienced, and unknown categories and conducted a comparative analysis of the attitude differences among these learner groups in MOOCs. To gain insights into learners' preferences and concerns, [PERSON] et al. [18] employed a combination of machine learning techniques and statistical analyses to mine reviews from NetEase, and they found learners pay more attention to the teaching and platform rather than the course content. [PERSON] et al. [37] identified the influencing factors of learner satisfaction through text mining techniques. [PERSON] et al. [38] introduced a comprehensive scoring methodology for MOOCs. This approach initially mined individuals' satisfaction levels with various course aspects from MOOC reviews, subsequently employing the Analytic Hierarchy Process (AHP) to generate an overall course rating. [PERSON] et al. [39] used a structural topic model in MOOC reviews and found that the topics vary depending on the emotions or categories of the courses involved. [PERSON] et al. [6] undertook a comprehensive investigation to identify the key factors that contribute to the success of MOOCs. They categorized MOOCs into two distinct types and conducted a detailed analysis to uncover the influencing factors associated with each type. [PERSON] et al. [9] performed an extensive analysis of a large corpus of review text data, uncovering that numeric ratings alone provide limited guidance to learners in the course selection process. However, they identified that text mining can be effectively employed to aid course selection, as MOOC reviews contain abundant valuable information. [PERSON] et al. [40] introduced a multi-stage text-mining-driven approach aimed at uncovering the influencing factors of learner satisfaction in MOOCs. Furthermore, to ensure the reliability and practical implications of their proposed method, they validated its efficacy through a survey methodology. Based on online reviews, [PERSON] et al. [41] discovered that not all online review topics exert an influence on overall learner satisfaction. It is evident that recent research on MOOC text mining has primarily focused on methodological studies, with a strong emphasis on extracting valuable content from MOOC reviews. Notably, there has been a significant focus on exploring the influencing factors of learner satisfaction. However, a notable gap exists in the literature regarding the provision of course improvement suggestions based on these mined influencing factors.