@revista_internacional{1018,
  keywords = {Product ranking, Online reviews, Multi-source data, q-rung orthopair fuzzy sets, Consumer psychology},
  author = {Peng Wu and Yutong Xie and Ligang Zhou and Muhammet Deveci and Luis Martínez},
  title = {Multi-source data driven decision-support model for product ranking with consumer psychology behavior},
  abstract = {In the context of information overload, leveraging online reviews for product ranking can enhance consumers’ decision-making efficiency and facilitate informed purchasing decisions. However, previous studies have mainly focused on online review data from a single source and do not capture consumer psychology behavior for scientific and reasonable product ranking. Therefore, this study proposes a multi-source data driven decision-support model for product ranking that considers consumer psychology behavior. In this model, after crawling and preprocessing multi-source data, text mining (including Latent Dirichlet Allocation (LDA) and sentiment analysis) is adopted to extract implicit evaluation criteria for the product and identify three sentiment polarities (e.g., positive, neutral, and negative), based on the sentiment analysis results they are represented as q-rung orthopair fuzzy numbers (q-ROFNs). Then, considering the interaction relationship between data, a q-rung orthopair fuzzy weighted geometric interaction averaging (q-ROFWGIA) aggregating function is defined to fuse multi-source data. In addition, explicit and implicit evaluation criteria of products are integrated to assess products, and sigmoid function is used to handle heterogeneous data. Furthermore, a regret theory-exponential TODIM (RT-ExpTODIM) ranking method is proposed. It can capture the loss avoidance and regret avoidance psychology of consumers during the product ranking process. Finally, a case study on ranking mobile phone through multi-source online reviews is provided to verify the validity of the proposed model.},
  year = {2025},
  journal = {Information Fusion},
  volume = {118},
  pages = {103014},
  issn = {1566-2535},
  url = {https://www.sciencedirect.com/science/article/pii/S1566253525000879},
  doi = {https://doi.org/10.1016/j.inffus.2025.103014},
}