This paper utilizes the deep learning model based on BERT-BiLSTM-CRF in combination with the econometric model to examine how hotel customers’ complaints toward diverse service attributes contribute to their overall satisfaction. With our model, seven types of customer complaints, including service, facility, cleanliness, price, location, dining, and noise, can be automatically identified from hotel online reviews, achieving an F1 of 0.82 and a recall of 0.85. Econometrics analyses show that different types of complaints have varying degrees of impact on customer satisfaction. For example, in the hotel industry, service complaints show a stronger negative effect than cleanliness complaints, facility complaints, etc. Furthermore, the results of the robustness check show that our conclusions are consistent before and after COVID-19. Our findings contribute to the customer dissatisfaction literature and offer practical implications for service failure management in online travel platforms.

中文翻译：

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了解来自在线酒店负面评论的客户投诉：一种基于 BERT 的深度学习方法


本文利用基于 BERT-BiLSTM-CRF 的深度学习模型与计量经济学模型相结合，研究了酒店客户对不同服务属性的投诉如何影响他们的整体满意度。通过我们的模型，可以从酒店在线评论中自动识别七种类型的客户投诉，包括服务、设施、清洁度、价格、位置、餐饮和噪音，实现 F1 为 0.82，召回率为 0.85。计量经济学分析表明，不同类型的投诉对客户满意度的影响程度不同。例如，在酒店行业，服务投诉比清洁投诉、设施投诉等表现出更强的负面影响。此外，稳健性检查的结果表明，我们在 COVID-19 前后的结论是一致的。我们的研究结果有助于客户不满文献，并为在线旅游平台的服务故障管理提供实际意义。