An Application to Measure Consensus on Customers' Ratings Using Hesitant Fuzzy Linguistic Term Sets

In this study, we propose a methodology using hesitant fuzzy linguistic term sets to aggregate rating values. The main idea is to define an integrated methodology for collaborative platforms involving individuals that use the same set of linguistic terms with different semantics. The objective is to fuse the opinions of different profiles of reviewers or customers when assessing an alternative. The proposed approach allows considering previous assessments to give personalized meaning to a set of linguistic terms. To this end, the concepts of linguistic perceptual maps and projections among perceptual maps are introduced to model and aggregate reviewers' different profiles. Finally, we compute a measure of consensus to capture the agreement among reviewers' opinions. All these concepts are applied in a real case example of rating books from Amazon to demonstrate the potential of the methodology in facilitating the evaluation and comparison of different alternatives. Using our methodology, the degree of consensus among customers can be used to rate in a more precise way the books before recommendation.