Signal in the Noise: Decoding the Reality of Airline Service Quality with Large Language Models

TL;DR

This paper demonstrates how large language models can analyze unstructured online reviews to uncover detailed insights into airline service quality, revealing hidden issues and satisfaction trends that traditional metrics miss.

Contribution

It introduces a novel LLM-based framework for extracting granular service insights from unstructured feedback, outperforming conventional survey methods.

Findings

Identified specific service issues like communication and staff conduct.

Detected satisfaction decline in key tourism markets post-2022.

Validated the framework's effectiveness as a diagnostic tool.

Abstract

Traditional service quality metrics often fail to capture the nuanced drivers of passenger satisfaction hidden within unstructured online feedback. This study validates a Large Language Model (LLM) framework designed to extract granular insights from such data. Analyzing over 16,000 TripAdvisor reviews for EgyptAir and Emirates (2016-2025), the study utilizes a multi-stage pipeline to categorize 36 specific service issues. The analysis uncovers a stark "operational perception disconnect" for EgyptAir: despite reported operational improvements, passenger satisfaction plummeted post-2022 (ratings < 2.0). Our approach identified specific drivers missed by conventional metrics-notably poor communication during disruptions and staff conduct-and pinpointed critical sentiment erosion in key tourism markets. These findings confirm the framework's efficacy as a powerful diagnostic tool, surpassing traditional surveys by transforming unstructured passenger voices into actionable strategic intelligence for the airline and tourism sectors.