Quantifying Airline Reputation from Multilingual Online Content: Artificial Intelligence and a Practical Application in a Lightweight Reputation-Intelligence Framework

This paper presents “Quantifying Airline Reputation from Multilingual Online Content: Artificial Intelligence and a Practical Application in a Lightweight Reputation-Intelligence Framework.” Airline reputation is increasingly shaped by multilingual digital narratives that evolve faster than conventional survey cycles, creating a need for timely and interpretable monitoring tools. This study develops and evaluates a lightweight reputation-intelligence framework that integrates brand-safe retrieval, multilingual transformer-based sentiment inference, zero-shot natural-language-inference aspect categorization, TF–IDF/KMeans topic induction, and short-horizon forecasting. The framework formalizes document-level outputs into managerial indicators, including a Net Sentiment Index, polarity shares, aspect scores, topic summaries, and projected sentiment trajectories. On a 3990-document annotated sentiment subset, the multilingual transformer achieved 0.9015 accuracy, 0.9048 macro-F1, 0.9050 weighted-F1, a Cohen’s kappa of 0.8492, and a Net Sentiment Index of 48.5%, while errors were concentrated between adjacent polarity classes. Aspect evaluation showed that supervised in-domain learning substantially outperformed zero-shot inference, clarifying the trade-off between cold-start portability and benchmark accuracy. The results support the framework as a pilot decision-support architecture for airline reputation sensing rather than as a definitive large-scale benchmark. The approach offers scalable and CPU-friendly monitoring capability for airlines, airports, consultants, and public-sector users, while future work should expand multilingual annotation and domain adaptation.