A Hybrid AI and Fuzzy MCDM Approach for Retailer Evaluation: Leveraging Sentiment Analysis and Expert Insights

Abstract

This study proposes a hybrid methodology for evaluating leading retail companies based on customer perspectives, combining Artificial Intelligence (AI)-driven sentiment analysis with fuzzy multiple criteria decision-making (MCDM). The framework integrates large-scale customer review analysis with expert decision-making to provide a comprehensive assessment of retail performance. The process begins with AI-based text mining to collect and analyze customer reviews, extracting emotional tones and identifying frequently mentioned criteria. Expert judgment is then applied to refine, organize, and assign importance to these criteria. The q-rung orthopair fuzzy set MCDM methodology is employed to address uncertainty, conflicting objectives, and qualitative expert opinions by translating them into a structured quantitative evaluation. This hybrid approach offers a balanced assessment that combines subjective and objective dimensions. As a case study, 2000 customer reviews from each of four major U.S. retailers—Amazon, Walmart, Costco, and Target—were analyzed to derive key evaluation criteria based on user feedback. The proposed method distinguishes itself through its unique integration of sentiment analysis and decision-makers' expert evaluations, enabling a holistic and robust evaluation of alternatives. By bridging customer perceptions with expert analysis, this methodology provides a deeper, more nuanced understanding of retailer performance, contributing to improved supplier selection and business decision-making processes. A second analysis, enabled by this methodology, also highlighted key performance differences among the retailers in areas such as customer service, delivery experience, and return/refund processes. Among the findings, Target and Amazon showed the strongest overall sentiment performance, while Costco excelled in return policies and Walmart exhibited weaker results in customer service and delivery. As a result, this hybrid methodology offers valuable insights for both decision-makers aiming to optimize supplier selection and customers seeking better shopping experiences.