Deep learning-driven sentiment analysis for customer satisfaction optimization in airline reviews

Purpose This research employs innovative deep learning techniques to perform sentiment analysis on airline customer feedback, systematically investigating service quality dimensions and customer satisfaction determinants through the theoretical lens of the SERVQUAL framework. Design/methodology/approach We analyzed airline reviews from TripAdvisor and Skytrax from July 2014 to July 2023 using SERVQUAL theory. Coarse-grained clustering revealed themes in tangibles and reliability, while fine-grained sentiment analysis, using our FusionBERT model, assessed responsiveness, assurance and empathy for deeper service quality insights. Findings Our approach outperformed baselines, offering a nuanced analysis of airline customer satisfaction using the SERVQUAL theory. Coarse-grained sentiment analysis assesses tangibles and reliability (facilities, service consistency), while fine-grained analysis evaluates responsiveness, assurance, and empathy (staff promptness, professionalism and care). This dual-layered method enables comprehensive service quality evaluation, helping airlines identify strengths and improvement areas. Originality/value This research provides actionable management solutions and recommendations for airline managers.