## 6 Conclusions  
Based on the learner's perspective, this paper presents a method that leverages text mining and multi-attribute decision-making frameworks to enhance the attributes of MOOCs, taking into account both non-emotional reviews and improvement costs. Initially, the method collected online reviews from MOOC platforms and extracted pertinent course attributes from these reviews. Subsequently, the review text was categorized into foursentiment groups--positive, neutral, negative, and non-emotional--utilizing deep learning techniques. To ascertain the improvement costs associated with each attribute, an expert voting mechanism was utilized. Finally, a decision matrix was constructed, and the TOPSIS method was employed to prioritize the attributes for improvement. To validate the effectiveness of our proposed method, it was applied to two programming courses: C language and Java language. The experimental results demonstrate the utility of our approach. The main contributions of this study are as follows:  
1. Regarding data selection, this study employed learners' online comments as the analytical dataset to establish a text mining model specifically tailored for MOOC online comments. The outcomes offer a real-time and visual representation of learners' preferences and needs. 2. Given the limited resources available, realizing their rational utilization holds significant importance in enhancing the overall quality of courses. Previous studies have primarily focused on determining the priority of attribute improvements solely based on learners' satisfaction levels, neglecting the crucial aspect of cost. Nevertheless, cost plays a pivotal role in the improvement process of course attributes. In scenarios where the cost of enhancing a specific attribute is prohibitively high, it becomes imperative to deliberate on whether to pursue such improvements despite their low satisfaction rating. Consequently, this study builds upon previous research, incorporating both cost considerations and non-emotional reviews, thereby enabling course managers to undertake a more comprehensive evaluation when aiming to enhance courses. 3. This study introduces a novel approach that integrates text mining and multi-attribute decision-making frameworks to effectively enhance MOOC attributes from the learners' perspective. By incorporating non-emotional reviews and considering improvement costs, our method offers a comprehensive and practical solution to course managers. The utilization of deep learning techniques in sentiment analysis ensures accurate categorization of reviews, while the expert voting mechanism provides a reliable estimation of improvement costs. The TOPSIS method then facilitates the prioritization of attributes for improvement, enabling course managers to make informed decisions. This approach not only addresses the challenges associated with managing overwhelming information but also ensures that limited resources are allocated efficiently. 4. Overall, at the theoretical level, this study contributes to enriching relevant research on MOOC improvement by adopting a learner-centric perspective. It also serves as a valuable reference for similar studies. From a managerial perspective, the utilization of text mining and multi-attribute decision-making techniques enables a more precise analysis of curriculum attributes, thus addressing the challenge of managing overwhelming information faced by course managers. Furthermore, it offers course managers a more comprehensive framework to prioritize the improvement of course attributes across six dimensions: positive emotion, neutral emotion, negative emotion, no emotion, attention, and cost. Methodology, P.Y. ; writing--original draft, P.Y. ; visualization, P.Y. ; Writing--review and editing, [PERSON] ([PERSON]) and Z.W. ; investigation, [PERSON] ([PERSON]) and X.C. ; formal analysis, [PERSON] ([PERSON]), Z.W. and X.C. ; supervision, [PERSON] ([PERSON]). All authors have read and agreed to the published version of the manuscript.