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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) 
Ponte, M., Nunes, Ana Catarina, Ramos, P., Cruz, F. & Vicente, P. (2026). Using Sentiment Analysis to Detect Operational Issues in a Holiday Rental Company. In Proceedings of the 8th International Conference on Advanced Research Methods and Analytics (CARMA 2026).: Editorial Universitat Politécnica de Valência.
Exportar Referência (IEEE) 
M. C. Ponte et al.,  "Using Sentiment Analysis to Detect Operational Issues in a Holiday Rental Company", in Proc. of the 8th Int. Conf. on Advanced Research Methods and Analytics (CARMA 2026), Editorial Universitat Politécnica de Valência, 2026
Exportar BibTeX 
@inproceedings{ponte2026_1784760330910,
	author = "Ponte, M. and Nunes, Ana Catarina and Ramos, P. and Cruz, F. and Vicente, P.",
	title = "Using Sentiment Analysis to Detect Operational Issues in a Holiday Rental Company",
	booktitle = "Proceedings of the 8th International Conference on Advanced Research Methods and Analytics (CARMA 2026)",
	year = "2026",
	editor = "",
	volume = "",
	number = "",
	series = "",
	doi = "10.4995/CARMA2026.2026.22447",
	publisher = "Editorial Universitat Politécnica de Valência",
	address = "",
	organization = ""
}
Exportar RIS 
TY  - CPAPER
TI  - Using Sentiment Analysis to Detect Operational Issues in a Holiday Rental Company
T2  - Proceedings of the 8th International Conference on Advanced Research Methods and Analytics (CARMA 2026)
AU  - Ponte, M.
AU  - Nunes, Ana Catarina
AU  - Ramos, P.
AU  - Cruz, F.
AU  - Vicente, P.
PY  - 2026
DO  - 10.4995/CARMA2026.2026.22447
AB  - Online reviews provide a wealth of information and feedback that businesses can use to help identify issues that require short term action and aid medium/long term decision-making. Numerical information such as global valoration and scores in specific evaluation aspects can be complemented by the insights from textual fields of the reviews. This study uses text analytics to address the large volume of online review data for a holiday rental company. Specifically, sentiment analysis is used to assess the polarity of the different text fields and topic detection is used to identify the underlying topics of the text data. This analysis allowed to identify relevant topics that are not covered by the evaluation aspects or correspond to specific issues related to more general evaluation aspects. Some reviews were also identified with large discrepancies between sentiment analysis and actual global valoration, which warrants attention from decision-makers.
ER  -