Many hotels target guest acquisition efforts to specific markets in order to best anticipate individual preferences and needs of their guests. Likewise, such strategic positioning is a prerequisite for efficient marketing budget allocation. Official statistics report on the number of visitors from different countries, but no fine-grained information on the guest composition of individual businesses exists. There is, however, growing interest in such data from competitors, suppliers, researchers and the general public. We demonstrate how machine learning can be leveraged to extract references to guest nationalities from unstructured text reviews in order to dynamically assess and monitor the dynamics of guest composition of individual businesses. In particular, we show that a rather simple architecture of pre-trained embeddings and stacked LSTM layers provides a better performance-runtime tradeoff than more complex state-of-the-art language models.

翻译：许多酒店将客户获取工作瞄准特定市场，以便最好地预测客人的个人偏好和需求。同样，这种战略定位是高效营销预算分配的先决条件。官方统计数据报告了来自不同国家的游客数量，但缺乏关于单个企业客人构成的细粒度信息。然而，竞争对手、供应商、研究人员和公众对此类数据的兴趣日益增长。我们展示了如何利用机器学习从非结构化文本评论中提取对客人国籍的引用，从而动态评估和监测单个企业客人构成的动态变化。特别地，我们证明，与更复杂的先进语言模型相比，由预训练嵌入和堆叠LSTM层组成的相对简单的架构能够提供更好的性能-运行时间权衡。