Online services such as Amazon, Tripadvisor, Ebay, etc., allow users to express sentiments about different products or services. Not only that, in some cases it is also possible to express sentiments about the different features characterizing those products or services. Most users express sentiments about individual features by using numerical values, which sometimes do not allow users to reflect properly what they are meaning and therefore they are misleading. To overcome this key issue and make users’ opinions in online services more comprehensive, a new methodology for representing sentiments using linguistic term sets instead of numerical values is presented. In addition, this methodology will allow to implement importance degrees on the different features characterizing users’ opinions. From both sentiments and importance of the features, the most important opinions for each user is derived via an aggregation step based on the Type-1 Ordered Weighted Averaging (T1OWA) operator, which is able to aggregate the corresponding fuzzy set representations of linguistic terms. Furthermore, the final output of the T1OWA based-search process can easily be interpreted by users because it is always of the same type (fuzzy) and defined in the same domain of the original fuzzy linguistic labels. A case study is presented where the T1OWA operator methodology is used to assess different opinions according to different user profiles.

诸如Amazon，Tripadvisor，Ebay等在线服务允许用户表达有关不同产品或服务的情绪。不仅如此，在某些情况下，还可以表达出对那些产品或服务所具有的不同特征的情感。大多数用户通过使用数值来表达对单个功能的感受，有时这些数值不能使用户正确反映其含义，因此会产生误导。为了克服这个关键问题，并使在线服务中的用户意见更加全面，提出了一种使用语言术语集代替数值来表示情感的新方法。另外，这种方法将允许对表征用户意见的不同特征实施重要性程度。从功能的重要性和重要性来看，每个用户最重要的意见是通过基于类型1有序加权平均（T1OWA）运算符的聚合步骤得出的，该运算符能够聚合语言术语的相应模糊集表示形式。此外，基于T1OWA的搜索过程的最终输出很容易被用户解释，因为它始终是同一类型（模糊）并且在原始模糊语言标签的同一域中定义。提出了一个案例研究，其中T1OWA运营商方法用于根据不同的用户配置文件评估不同的意见。基于T1OWA的搜索过程的最终输出很容易被用户解释，因为它始终是同一类型（模糊）并且在原始模糊语言标签的同一域中定义。提出了一个案例研究，其中T1OWA运营商方法用于根据不同的用户配置文件评估不同的意见。基于T1OWA的搜索过程的最终输出很容易被用户解释，因为它始终是同一类型（模糊）并且在原始模糊语言标签的同一域中定义。提出了一个案例研究，其中T1OWA运营商方法用于根据不同的用户配置文件评估不同的意见。