用大模型分析1.6万条航评，发现航空公司运营改善但乘客满意度反而暴跌。
Signal in the Noise: Decoding the Reality of Airline Service Quality with Large Language Models
- 用多阶段流程从航评中提取36类服务问题。
- 发现埃及航空2022年后评分低于2.0，沟通差是主因。
- 适合关注旅客体验与危机预警的航司及文旅机构。
传统服务质量指标难以捕捉在线反馈中隐藏的乘客满意度深层动因。本研究验证了一种用于解析此类数据的大语言模型框架。通过分析埃及航空与阿联酋航空（2016–2025年）超过1.6万条TripAdvisor评论，采用多阶段流程对36项具体服务问题进行分类。结果显示，埃及航空存在显著的“运营感知脱节”：尽管运营状况有所改善，但2022年后乘客满意度急剧下降（评分<2.0）。该方法识别出传统指标忽略的关键因素——如延误时沟通不畅和员工态度问题，并揭示了主要旅游市场中的情绪恶化。研究证实该框架能有效将非结构化乘客声音转化为可操作的战略情报，优于传统问卷调查，为航空与旅游行业提供强大诊断工具。
原文摘要 · Abstract (English)
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.
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