## 2 Related Work
### Review Text-Mining-Based Product or Service Improvement  
Online reviews encompass consumers' varying degrees of satisfaction and dissatisfaction towards various products or services. Consequently, numerous studies have been devoted to the text mining of online reviews, aiming to facilitate product enhancement and the creation of innovative offerings.  
[PERSON] et al. [24] utilized Latent Dirichlet Allocation (LDA) [42] to extract product attributes from Amazon reviews. Subsequently, they employed a rule-based sentiment analysis approach to categorize the sentiment associated with these attributes. Finally, they leveraged the Kano model to identify the attributes that required improvement. Their approach is a typical approach of text-mining-based product improvement, such as [PERSON] et al. [25]. [PERSON] et al. [43] enhanced the efficacy of the traditional Kano model for product improvement by integrating it with the fuzzy Analytic Hierarchy Process (AHP) and Quality Function Deployment (QFD). This integrated approach provided a comprehensive framework for prioritizing and optimizing product attributes, leading to more targeted and effective product enhancements. [PERSON] et al. [44] undertook a comprehensive text mining analysis of high-speed rail reviews, successfully extracting six passenger demands. Utilizing large-scale group decision-making techniques, they aggregated the satisfaction levels of 100 passengers across these six demands, finally deriving the six demands' degrees of satisfaction and rankings. [PERSON] et al. [22] took into account both explicit and implicit features during the extraction of product attributes. They employed neural network training to determine the relative importance of each feature, subsequently mapping these attributes to the Kano model. Previous studies employing text mining and the Kano model have not considered the consumers' affective needs. In order to address this issue, [PERSON] et al. [45] adopted the Kansei-integrated Kano method to obtain the priority of product feature improvement. This method incorporates affective design and is more comprehensive than the traditional Kano model. [PERSON] et al. [46] used the attribute mapping framework introduced by [PERSON] and [PERSON], successfully extracting attributes from reviews to offer innovation insights. Their approach to mining attributes from online reviews surpasses previous methods in terms of depth and precision. [PERSON] et al. [47] combined the Latent Dirichlet Allocation (LDA) topic model and Quality Function Deployment (QFD) to mine customer requirements and competition information from online reviews for new product development. [PERSON] et al. [48] proposed a large-scale group decision-making method to integrate the satisfaction of individual consumers on product attributes to form group satisfaction. [PERSON] and [PERSON] [49] utilized text mining techniques to cluster consumers based on their preferences. Initially, they mined the product attributes that consumers prioritize from online reviews. Subsequently, they employed neural networks to determine the importance of these attributes and clustered consumers accordingly, leveraging the attribute importance weights. [PERSON] and [PERSON] [50] combined text mining and large group decision-making methods to make product improvement decisions. Through a comparative analysis with intuitive and expert decision-making approaches, they demonstrated the reliability and effectiveness of their proposed method, surpassing both comparison methods. While numerous studies have primarily centered on enhancing products or services through text mining, often neglecting non-emotional content and the associated costs of attribute improvement, they primarily focus on consumer attention and satisfaction.