MUSAsent: Integrating aspect-based sentiment into Multicriteria Satisfaction Analysis
提出MUSAsent模型，将文本情感极性作为约束嵌入MUSA方法的线性规划中，统一处理评分与非结构化反馈，提升满意度评估的稳定性和可解释性，适用于酒店、航空等服务行业的管理决策。
This paper proposes MUSAsent, the first preference-disaggregation model to embed aspect-based sentiment directly into the linear programming structure of the MUSA method. By incorporating textual polarity signals as constraints that guide the estimation of partial satisfaction functions, MUSAsent enables a unified treatment of ratings and unstructured feedback while preserving the additive and monotonic properties of MUSA. We examine two variants in which Model I adjusts only the partial satisfaction functions and Model II extends these adjustments to the global function as well. Using thirteen real-world datasets spanning airlines, airports, lounges, hotels, online education, and beverage reviews, the study shows that Model I consistently increases post-optimality stability without degrading model fit. These findings highlight the methodological value of integrating sentiment within the core estimation process and demonstrate the practical benefit of obtaining more stable and interpretable satisfaction assessments for managerial decision-making.