A novel online reviews-based decision-making framework to manage rating and textual reviews
Rating and textual reviews are two of the most commonly used online reviews. However, most of existing studies utilize them separately to conduct decision analysis. Therefore, this paper proposes a novel Online Reviews-based Decision-Making (ORDM) framework that incorporates both kinds of reviews, w...
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Published in | Expert systems with applications Vol. 259; p. 125367 |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
Published |
Elsevier Ltd
01.01.2025
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Subjects | |
Online Access | Get full text |
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Abstract | Rating and textual reviews are two of the most commonly used online reviews. However, most of existing studies utilize them separately to conduct decision analysis. Therefore, this paper proposes a novel Online Reviews-based Decision-Making (ORDM) framework that incorporates both kinds of reviews, which can effectively model and manage the inherent uncertainty and fuzziness in hybrid reviews. To do so, we first define a method to model hybrid online reviews as interval values. Then, an automatic Consensus-Reaching Process (CRP) developed from a novel Minimum Cost Consensus (MCC) model for interval values, is applied to obtain an agreed collective opinion. Finally, a new interval Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method is defined to compare and rank alternatives. A case study is then presented to illustrate the feasibility of this approach. Additionally, sensitivity analysis and discussions are further conducted to discuss the influence of parameters and the superiority of the proposed method.
•Incorporate hybrid online reviews to assist in decision-making.•Define an information transformation method to handle hybrid online reviews.•Develop a minimum consensus cost model for handling interval information.•Propose an interval TOPSIS method based on total orders. |
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AbstractList | Rating and textual reviews are two of the most commonly used online reviews. However, most of existing studies utilize them separately to conduct decision analysis. Therefore, this paper proposes a novel Online Reviews-based Decision-Making (ORDM) framework that incorporates both kinds of reviews, which can effectively model and manage the inherent uncertainty and fuzziness in hybrid reviews. To do so, we first define a method to model hybrid online reviews as interval values. Then, an automatic Consensus-Reaching Process (CRP) developed from a novel Minimum Cost Consensus (MCC) model for interval values, is applied to obtain an agreed collective opinion. Finally, a new interval Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method is defined to compare and rank alternatives. A case study is then presented to illustrate the feasibility of this approach. Additionally, sensitivity analysis and discussions are further conducted to discuss the influence of parameters and the superiority of the proposed method.
•Incorporate hybrid online reviews to assist in decision-making.•Define an information transformation method to handle hybrid online reviews.•Develop a minimum consensus cost model for handling interval information.•Propose an interval TOPSIS method based on total orders. |
ArticleNumber | 125367 |
Author | Wang, Ying-Ming Martínez, Luis García-Zamora, Diego Pan, Xiao-Hong He, Shi-Fan |
Author_xml | – sequence: 1 givenname: Xiao-Hong orcidid: 0000-0003-2898-6716 surname: Pan fullname: Pan, Xiao-Hong email: xiaohongpan0303@126.com organization: School of Business, Qingdao University, Shandong, 266071, China – sequence: 2 givenname: Shi-Fan orcidid: 0000-0003-4652-8620 surname: He fullname: He, Shi-Fan email: shifanhe0828@163.com organization: School of Business, Qingdao University, Shandong, 266071, China – sequence: 3 givenname: Diego orcidid: 0000-0002-0843-4714 surname: García-Zamora fullname: García-Zamora, Diego email: dgzamora@ujaen.es organization: Department of Computer Science, University of Jaén, Jaén, 23071, Spain – sequence: 4 givenname: Ying-Ming orcidid: 0000-0002-5229-0914 surname: Wang fullname: Wang, Ying-Ming email: ymwang@fzu.edu.cn organization: Decision Sciences Institute, Fuzhou University, Fujian, 350108, China – sequence: 5 givenname: Luis orcidid: 0000-0003-4245-8813 surname: Martínez fullname: Martínez, Luis email: martin@ujaen.es organization: Department of Computer Science, University of Jaén, Jaén, 23071, Spain |
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