Assessing Guest Nationality Composition from Hotel Reviews

TL;DR

This paper presents a machine learning approach to analyze hotel reviews for extracting guest nationality information, enabling hotels to monitor guest composition dynamically and efficiently.

Contribution

It introduces a simple yet effective neural network architecture using pre-trained embeddings and LSTM layers for nationality extraction from reviews.

Findings

The proposed model outperforms more complex language models in performance and speed.

It enables dynamic assessment of hotel guest nationality composition.

The approach provides a practical tool for strategic marketing and competitive analysis.

Abstract

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.