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Dealing with incomplete information in linguistic group decision making by means of Interval Type‐2 Fuzzy Sets
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In: ISSN: 0884-8173 ; EISSN: 1098-111X ; International Journal of Intelligent Systems ; https://www.hal.inserm.fr/inserm-03026626 ; International Journal of Intelligent Systems, Wiley, 2019, 34 (6), pp.1261-1280. ⟨10.1002/int.22095⟩ (2019)
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Stochastic logistic fuzzy maps for the construction of integrated multirates scenarios in the financing of infrastructure projects
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A T1OWA Fuzzy Linguistic Aggregation Methodology for Searching Feature-based Opinions.
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Abstract:
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link. ; 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.
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Keyword:
Feature opinion; Feature opinion aggregation; Linguistic model; Sentiment analysis; T1OWA aggregation operator
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URL: https://doi.org/10.1016/j.knosys.2019.105131
https://dora.dmu.ac.uk/handle/2086/18791
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Dealing with Incomplete Information in Linguistic Group Decision Making by Means of Interval Type-2 Fuzzy Sets
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