Extracting knowledge from customer reviews: an integrated framework for digital platform analytics

Abstract

AbstractOnline review sites play a crucial role in shaping consumer purchasing decisions, making the analysis of customer feedback essential for businesses. Given the complexity of these reviews, often including both quantitative and qualitative data, advanced analytical frameworks are necessary. To this end, this paper introduces an integrated framework for customer feedback analysis, combining aspect‐based sentiment analysis, multicriteria decision‐making, and a fuzzy rule‐based approach. The proposed system effectively processes both textual and numerical data from online reviews, enabling the extraction of actionable insights. To demonstrate its practical utility, we apply it to a real‐world dataset from a major European airline. The results illustrate the framework's effectiveness in identifying key factors influencing customer satisfaction and pinpointing areas needing improvement. Additionally, data‐driven recommendations are provided to support business decision‐making and enable the customization of products and services to better meet customer expectations.