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Extracting knowledge from customer reviews: an integrated framework for digital platform analytics
Author(s) -
Kyriakidis Anastasios,
Tsafarakis Stelios
Publication year - 2025
Publication title -
international transactions in operational research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.032
H-Index - 52
eISSN - 1475-3995
pISSN - 0969-6016
DOI - 10.1111/itor.13537
Subject(s) - computer science , purchasing , key (lock) , sentiment analysis , personalization , analytics , customer satisfaction , data science , voice of the customer , big data , customer intelligence , knowledge management , fuzzy logic , process management , management science , customer retention , data mining , artificial intelligence , business , marketing , service quality , world wide web , service (business) , engineering , computer security
Abstract Online 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.