A novel online reviews-based decision-making framework to manage rating and textual reviews

• Published in: Expert systems with applications Vol. 259; p. 125367
• Main Authors: Pan, Xiao-Hong, He, Shi-Fan, García-Zamora, Diego, Wang, Ying-Ming, Martínez, Luis
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.01.2025
• Subjects: Hybrid online reviews; Interval minimum cost consensus; Interval TOPSIS; Online reviews-based decision-making; Hybrid online reviews; Interval TOPSIS; Online reviews-based decision-making; Interval minimum cost consensus
• ISSN: 0957-4174
• DOI: 10.1016/j.eswa.2024.125367

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.