## 2 Related Work
### Review the Ranking Method of Multi-Attribute Decision-Making  
Whether it be a purchase decision in daily life or a task scheduling decision at work, decision-making forms a crucial aspect of people's daily lives and professional endeavors [51]. Some decisions can be relatively straightforward, with minor consequences if a wrong choice is made. However, other decisions can be highly intricate, such that even a slight error can lead to significant repercussions, necessitating a profound and cautious approach. Generally speaking, a decision problem in real life typically involves numerous criteria or attributes that must be taken into account concurrently to arrive at a well-informed decision. The exploration of such challenges is frequently labeled as multi-criterion decision-making or multi-attribute decision-making, which entails selecting the most suitable option from a finite set of alternatives.  
The literature introduces some of the most renowned MADM methods, including PROMETHEE, TODIM, VIKOR, and TOPSIS, which are designed to tackle ranking problems effectively [52; 53; 54; 55]. [PERSON] et al. [56] initially extracted product attributes, weight values, and emotional tendencies from online reviews. Subsequently, they employed the Random Multi-criteria Acceptance Analysis (SMAA)-PROMETHEE method, to derive product ranking outcomes. [PERSON] et al. [57] proposed an innovative product selection model that integrates sentiment analysis with the intuitionistic fuzzy TODIM method. This model aims to assist potential customers in ranking alternative products based on consumers' opinions regarding product performance. [PERSON] et al. [51] introduced a quantitative approach for hotel selection leveraging online reviews. Furthermore, they innovatively developed the DL-VIKOR method, which ranks hotels based on customer satisfaction scores and the weights of extracted attributes. [PERSON] et al. [58] conducted a cluster analysis using self-organizing mapping (SOM) to categorize hotel features. Subsequently, they employed the similarity to Ideal Solution Prioritization technique (TOPSIS) to rank these features. Additionally, neural fuzzy technology was utilized to reveal customer satisfaction levels, providing a comprehensive understanding of hotel performance. [PERSON] et al. [55] introduced a novel approach for product ranking that integrates the mining of online reviews with an interval-valued intuitionistic fuzzy technique (TOPSIS). This method aims to assist consumers in selecting products that align with their individual preferences.  
No single multi-attribute decision analysis method (MADM) can be unilaterally designated as the best or worst, as each has its own unique strengths and limitations [59]. Each MADM method possesses distinct advantages and limitations, and its effectiveness depends heavily on how it is tailored to specific outcomes and objectives within the planning process. Consequently, the selection of an appropriate MADM model should be guided by specific scenarios and requirements rather than solely relying on general evaluations. TOPSIS, originally developed by [PERSON] and [PERSON] in 1981, is a straightforward ranking method, both conceptually and in terms of its application [60]. TOPSIS offers three notable benefits: it is comprehensive; it requires minimal data; and it produces intuitive and easily comprehensible results [55]. Given its excellent performance in our investigation [61], the method combining the interval intuitionistic fuzzy set with TOPSIS was chosen as the ranking method for this study.  
After considering the above studies, we first use text mining techniques to extract the attributes that learners pay attention to, the corresponding emotional tendency and attention degree of the attributes from online reviews, and determine the satisfaction degree of the attributes. We then use a multi-attribute decision-making approach to prioritize attribute improvements.