{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T22:50:38Z","timestamp":1778280638256,"version":"3.51.4"},"reference-count":18,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2022,11,11]],"date-time":"2022-11-11T00:00:00Z","timestamp":1668124800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Digital"],"abstract":"<jats:p>In recent years, Natural Language Processing (NLP) has become increasingly important for extracting new insights from unstructured text data, and pre-trained language models now have the ability to perform state-of-the-art tasks like topic modeling, text classification, or sentiment analysis. Currently, BERT is the most widespread and widely used model, but it has been shown that a potential to optimize BERT can be applied to domain-specific contexts. While a number of BERT models that improve downstream tasks\u2019 performance for other domains already exist, an optimized BERT model for tourism has yet to be revealed. This study thus aimed to develop and evaluate TourBERT, a pre-trained BERT model for the tourism industry. It was trained from scratch and outperforms BERT-Base in all tourism-specific evaluations. Therefore, this study makes an essential contribution to the growing importance of NLP in tourism by providing an open-source BERT model adapted to tourism requirements and particularities.<\/jats:p>","DOI":"10.3390\/digital2040030","type":"journal-article","created":{"date-parts":[[2022,11,14]],"date-time":"2022-11-14T04:30:52Z","timestamp":1668400252000},"page":"546-559","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["When BERT Started Traveling: TourBERT\u2014A Natural Language Processing Model for the Travel Industry"],"prefix":"10.3390","volume":"2","author":[{"given":"Veronika","family":"Arefeva","sequence":"first","affiliation":[{"name":"Institute of Business Informatics, Johannes Keppler University of Linz, 4040 Linz, Austria"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4888-6026","authenticated-orcid":false,"given":"Roman","family":"Egger","sequence":"additional","affiliation":[{"name":"Department of Innovation and Management in Tourism, Salzburg University of Applied Sciences, 5412 Salzburg, Austria"},{"name":"Department of Tourism and Service Management, Modul University Vienna, 1190 Wien, Austria"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"557","DOI":"10.1016\/S0261-5177(02)00014-6","article-title":"Evaluating the use of the Web for tourism marketing: A case study from New Zealand","volume":"23","author":"Doolin","year":"2002","journal-title":"Tour. Manag."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Yu, J., and Egger, R. (2021). Tourist Experiences at Overcrowded Attractions: A Text Analytics Approach. Information and Communication Technologies in Tourism 2021, Springer.","DOI":"10.1007\/978-3-030-65785-7_21"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Daxb\u00f6ck, J., Dulbecco, M.L., Kursite, S., Nilsen, T.K., Rus, A.D., Yu, J., and Egger, R. (2021). The Implicit and Explicit Motivations of Tourist Behaviour in Sharing Travel Photographs on Instagram: A Path and Cluster Analysis. Information and Communication Technologies in Tourism 2021, Springer.","DOI":"10.1007\/978-3-030-65785-7_22"},{"key":"ref_4","unstructured":"Saraiva, J.P.D.P.M. (2013). Web 2.0 in restaurants: Insights regarding TripAdvisor\u2019s use in Lisbon. [Doctoral Dissertation, Universidade Catolica Protugesa]."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Egger, R. (2022). Natural Language Processing: An Introduction. Applied Data Science in Tourism. Interdisciplinary Approaches, Methodologies and Applications, Springer.","DOI":"10.1007\/978-3-030-88389-8_15"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Wennker, P. (2020). K\u00fcnstliche Intelligenz in der Praxis. Anwendung in Unternehmen und Branchen: KI wettbewerbs- und zukunftsorientiert Einsetzen, Springer Gabler. Available online: https:\/\/ebookcentral.proquest.com\/lib\/kxp\/detail.action?docID=6326361.","DOI":"10.1007\/978-3-658-30480-5"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Poon, A. (1993). Tourism, Technology and Competitive Strategies, CAB International.","DOI":"10.1079\/9780851989501.0000"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Egger, R. (2022). Text Representations and Word Embeddings. Vectorizing Textual Data. Applied Data Science in Tourism. 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(2021, June 02). 515K Hotel Reviews Data in Europe. Available online: https:\/\/www.kaggle.com\/jiashenliu\/515k-hotel-reviews-data-in-europe."}],"container-title":["Digital"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2673-6470\/2\/4\/30\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:16:35Z","timestamp":1760145395000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2673-6470\/2\/4\/30"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,11]]},"references-count":18,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["digital2040030"],"URL":"https:\/\/doi.org\/10.3390\/digital2040030","relation":{},"ISSN":["2673-6470"],"issn-type":[{"value":"2673-6470","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,11]]}}}