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Topic Modelling of Tourist Dining Experiences Based on the GLOBE Model

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Abstract

The needs of travellers vary across cultures. When it comes to culinary aspects, there is a strong connection between gastronomy and culture. To optimise service offerings, investigation of the essential aspects of dining experiences in relation to cultural backgrounds is of great importance. In the age of digitalisation, tourists share their dining experiences throughout their multiphasic travel journey via online platforms. By considering nine distinct cultural backgrounds, this research aims to investigate tourist experiences based on TripAdvisor restaurant reviews through topic modelling, using the city of Salzburg as its study context. Depending on one’s cultural circumstances, the findings demonstrate that the most important aspects include staff, food-menu items, value for money, restaurant physical appearance, food authenticity, overall service, menu offers, food quality, atmosphere, and recommendations. This study advances the state-of-the-art knowledge of societal culture as a variable in the target market analysis of restaurant customers. Findings allow restaurant owners, other tourism service providers, and destination management organisations to analyse and adapt their service offerings and strategies accordingly.
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Due to the rapid growth of texts in today’s society, much of which is produced via online social networks in the form of user-generated content, extracting useful information from unstructured text poses quite a challenge. However, thanks to the rapid development of natural language processing algorithms, including topic modelling techniques that help to discover latent topics in text documents such as online reviews or Twitter and Facebook posts, this challenge can be confronted. As such, topic modelling approaches have been gaining popularity in the field of tourism; yet, often little insight is given into the creation process and the quality of topic modeling results. Thus, this chapter aims to introduce several topic modelling algorithms, to explain their intuition in a brief and concise manner, and to provide tips and hints in relation to the necessary (pre-) processing steps, proper hyperparameter tuning, and comprehensible evaluation of the results.
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