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A publicação pode ser exportada nos seguintes formatos: referência da APA (American Psychological Association), referência do IEEE (Institute of Electrical and Electronics Engineers), BibTeX e RIS.

Exportar Referência (APA) 
Ana Luiza Beck, Bilro, R.G. & Loureiro, S. M. C. (2025). Unveiling the motivations: a text-mining analysis of tourist motivations in the heart of Europe. In Nikolaos Stylos; Jeremy Zwiegelaar;  (Ed.), Handbook on Big Data Marketing and Management in Tourism and Hospitality. (pp. 56-74). Cheltenham, UK: Edward Elgar Publishing.
Exportar Referência (IEEE) 
A. L. Beck et al.,  "Unveiling the motivations: a text-mining analysis of tourist motivations in the heart of Europe", in Handbook on Big Data Marketing and Management in Tourism and Hospitality, Nikolaos Stylos; Jeremy Zwiegelaar; , Ed., Cheltenham, UK, Edward Elgar Publishing, 2025, pp. 56-74
Exportar BibTeX 
@incollection{beck2025_1764928306391,
	author = "Ana Luiza Beck and Bilro, R.G. and Loureiro, S. M. C.",
	title = "Unveiling the motivations: a text-mining analysis of tourist motivations in the heart of Europe",
	chapter = "",
	booktitle = "Handbook on Big Data Marketing and Management in Tourism and Hospitality",
	year = "2025",
	volume = "",
	series = "",
	edition = "",
	pages = "56-56",
	publisher = "Edward Elgar Publishing",
	address = "Cheltenham, UK",
	url = "https://www.elgaronline.com/edcollchap/book/9781035300136/chapter4.xml"
}
Exportar RIS 
TY  - CHAP
TI  - Unveiling the motivations: a text-mining analysis of tourist motivations in the heart of Europe
T2  - Handbook on Big Data Marketing and Management in Tourism and Hospitality
AU  - Ana Luiza Beck
AU  - Bilro, R.G.
AU  - Loureiro, S. M. C.
PY  - 2025
SP  - 56-74
DO  - 10.4337/9781035300136.00010
CY  - Cheltenham, UK
UR  - https://www.elgaronline.com/edcollchap/book/9781035300136/chapter4.xml
AB  - This chapter examines tourist perceptions of cultural attractions in Central European capital cities, identifying their top attractions, highlighting similarities and differences in the tourism experience, and providing recommendations for tourism planning organisations. Although the massive amount of data available makes it more difficult for businesses and stakeholders to control the information regarding their product or service, it also opens up the opportunity to analyse the user-generated content and learn from the visitors’ experiences, enhancing or fixing the aspects that generate discontent, as well as emphasising the good practices in place and predicting the likelihood of future purchases. Text-mining techniques pose as valuable tools to process this intensive volume of information. A netnographic methodology is employed, using sentiment analysis of TripAdvisor reviews for six cities: Berlin; Bratislava; Budapest; Prague; Vienna; and Warsaw. The results reveal the General and Topic Sentiment Analyses for each city, showcasing the main aspects of the tourism experience as mentioned in the comments. All cities had a positive perception, with Budapest, Prague, and Vienna performing exceptionally well. However, concerns about over-tourism were evident in the “People” topic. Berlin had the lowest average polarity, indicating the need for a closer look at the city's management as a cultural destination.
ER  -