  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.
