Fierce competition in the global market forces companies to satisfy all aspects of customers’ needs during the product design stage. Among the customers’ needs, affective needs are difficult to satisfy since understanding the affective needs of customers is a challenging task. Therefore, Kansei Engineering (KE), which is capable of transforming the affective needs of customers into product design form elements, has been widely used in literature. In KE, there are two types of systems: Forward KE and Backward KE. In the forward KE, Kansei words are inputs of the system and product design form elements are outputs of the system, while the product design form elements are inputs and Kansei words are outputs of the system in the backward KE. In this study, fuzzy linguistic summarization is proposed to extract fuzzy rules in the form of “if–then” rules that associate customers’ affective needs into product design form elements for both backward and forward KE. The brute force approach and genetic algorithm (GA) are used to obtain the most useful linguistic summaries supported by enough data, efficiently. Furthermore, fuzzy association rule mining using the Apriori algorithm is employed to compare the obtained results of fuzzy linguistic summarization. A case study is conducted on cradle design to illustrate the applicability of the proposed fuzzy linguistic summarization and the fuzzy association rule mining. Even though the brute force approach is the best option to generate linguistic summaries, it could not be efficiently used in the design of complex products since its time complexity is exponential; and therefore, GA could be used to generate linguistic summaries in an efficient way when time complexity of the approaches is compared. The results show that fuzzy linguistic summarization is an effective and powerful tool to capture the affective needs of customers.

全球市场的激烈竞争迫使公司在产品设计阶段满足客户各个方面的需求。在客户需求中，情感需求很难满足，因为了解客户的情感需求是一项艰巨的任务。因此，能够将顾客的情感需求转化为产品设计形式元素的感性工程（KE）在文献中得到了广泛的应用。在 KE 中，有两种类型的系统：前向 KE 和后向 KE。在正向KE中，感性词是系统的输入，产品设计形式元素是系统的输出；而在反向KE中，产品设计形式元素是输入，感性词是系统的输出。在这项研究中，模糊语言概括被提出以“if-then”规则的形式提取模糊规则，将客户的情感需求关联到产品设计形式元素中，用于后向和前向 KE。蛮力方法和遗传算法 (GA) 用于有效地获得由足够数据支持的最有用的语言摘要。此外，采用Apriori算法进行模糊关联规则挖掘，对模糊语言概括的结果进行比较。对摇篮设计进行了案例研究，以说明所提出的模糊语言摘要和模糊关联规则挖掘的适用性。尽管蛮力方法是生成语言摘要的最佳选择，由于其时间复杂度是指数级的，因此无法有效地用于复杂产品的设计；因此，当比较方法的时间复杂度时，GA 可用于以有效的方式生成语言摘要。结果表明，模糊语言摘要是捕捉客户情感需求的有效且强大的工具。