Ranking products through online reviews: A novel data-driven method based on interval type-2 fuzzy sets and sentiment analysis

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dc.contributor.authorQin, Jindong
dc.contributor.authorZeng, Mingzhi
dc.contributor.authorWei, Xiao
dc.contributor.authorPedrycz, Witold
dc.date.accessioned2024-05-19T14:46:18Z
dc.date.available2024-05-19T14:46:18Z
dc.date.issued2024
dc.departmentİstinye Üniversitesien_US
dc.description.abstractAs an essential information resource, online reviews play an important role in consumers' decision-making processes. To solve the product ranking problem through online reviews, two important issues are involved: sentiment analysis (SA) for online reviews and product ranking based on multi-criteria decision-making (MCDM) methods. However, merely a few studies have considered the impact of SA accuracy, which can significantly affect the final decision-making process. This paper proposes a novel data-driven method for ranking products through online reviews based on interval type-2 fuzzy sets (IT2FSs) and SA. In this method, after acquiring online reviews, the explicit and implicit attributes are extracted from the website itself and the latent Dirichlet allocation (LDA) model, respectively. Thereafter, a deep learning model is adopted to identify the five sentiment intensities of online reviews, based on which the SA results are represented as IT2FSs by considering the classification effect. After type-reduction for IT2FSs, the ranking order is obtained based on the exponential TODIM (ExpTODIM) method. Furthermore, a case study on ranking travel products from Trip.com Group through online reviews is provided to illustrate the effectiveness and applicability of the proposed method.en_US
dc.description.sponsorshipNational Natural Science Foundation of China (NSFC) [72071151]en_US
dc.description.sponsorshipThe work is supported by the National Natural Science Foundation of China (NSFC) under Project 72071151.en_US
dc.identifier.doi10.1080/01605682.2023.2215823
dc.identifier.endpage873en_US
dc.identifier.issn0160-5682
dc.identifier.issn1476-9360
dc.identifier.issue5en_US
dc.identifier.scopus2-s2.0-85160515401en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage860en_US
dc.identifier.urihttps://doi.org10.1080/01605682.2023.2215823
dc.identifier.urihttps://hdl.handle.net/20.500.12713/5493
dc.identifier.volume75en_US
dc.identifier.wosWOS:000993367100001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherTaylor & Francis Ltden_US
dc.relation.ispartofJournal of the Operational Research Societyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240519_kaen_US
dc.subjectProduct Rankingen_US
dc.subjectOnline Reviewsen_US
dc.subjectSentiment Analysisen_US
dc.subjectInterval Type-2 Fuzzy Setsen_US
dc.subjectExptodimen_US
dc.titleRanking products through online reviews: A novel data-driven method based on interval type-2 fuzzy sets and sentiment analysisen_US
dc.typeArticleen_US

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