@revista_internacional{957,
  keywords = {Multi-criteria decision-making, Online reviews, Probabilistic linguistic term set with interval uncertainty, Weight determination, Evidential reasoning algorithm},
  author = {Shi-Fan He and Xiao-Hong Pan and Ying-Ming Wang and Diego García-Zamora and Luis Martínez},
  title = {A novel Multi-Criteria Decision Making framework based on Evidential Reasoning dealing with missing information from online reviews},
  abstract = {Online reviews are a valuable tool for Multi-Criteria Decision Making (MCDM) because they provide insightful information on several aspects. However, not all reviews yield complete information on all the necessary criteria for the MCDM problem. In practice, users might not effectively analyze all of them due to a lack of knowledge or time constraints. Despite this shortcoming, these online reviews still offer useful information that would be lost if neglected. Therefore, this paper proposes a framework that offers a new approach to using online reviews in MCDM and effectively addresses missing information on a given criterion or criteria. This new approach first establishes an information processing mechanism to transform online reviews into Probabilistic Linguistic Term Sets (PLTSs) with interval uncertainty. Afterward, a weight determination model is defined to calculate the criteria weights, which are given as interval values to reflect the uncertainty of the criteria and consider information from online reviews to better capture the opinions of reviewers. Next, the Evidential Reasoning (ER) algorithm is extended to the PLTSs with interval uncertainty environment to fuse the evaluation information and generate the interval-valued expected utilities of the alternatives by considering the uncertainty related to the missing information. Finally, a case study is conducted to illustrate the implementation process of the proposed framework, and a comparative analysis is carried out to evaluate its performance.},
  year = {2024},
  journal = {Information Fusion},
  volume = {106},
  pages = {102264},
  issn = {1566-2535},
  url = {https://www.sciencedirect.com/science/article/pii/S1566253524000423},
  doi = {https://doi.org/10.1016/j.inffus.2024.102264},
}