{"ID":2822883,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2603.04404","arxiv_id":"2603.04404","title":"Signal in the Noise: Decoding the Reality of Airline Service Quality with Large Language Models","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 \u003c 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.","short_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...","url_abs":"https://arxiv.org/abs/2603.04404","url_pdf":"https://arxiv.org/pdf/2603.04404v1","authors":"[\"Ahmed Dawoud\",\"Osama El-Shamy\",\"Ahmed Habashy\"]","published":"2026-01-04T13:05:13Z","proceeding":"cs.IR","tasks":"[\"cs.IR\",\"cs.CL\",\"cs.CY\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}