2026-05-28T09:59:05Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/304402026-02-03T10:56:25Zcom_10630_2254col_10630_37953

Understanding destination brand experience through data mining and machine learning Calderón Fajardo, Víctor Anaya-Sánchez, Rafael Molinillo-Jiménez, Sebastián Minería de datos Turismo Aprendizaje automático (Inteligencia artificial) This research formalises a new methodology to measure and analyse Destination Brand Experience, improving upon traditional approaches by offering greater objectivity and rigour. Adopting a case study approach, five distinct and complementary types of analysis have been conducted: comprehensive sentiment analysis and topic modelling, an analysis using multiple thesauri, statistical analyses for hypothesis testing, and machine learning for classification. The methodological innovation, through the construction of thesauri, has enabled the measurement of sensory, affective, intellectual, and behavioural dimensions in unique and emblematic attractions, experiences, and transportation within a tourist destination, based on visitor reviews. This new approach allows tourism professionals and destination managers to identify areas for improvement and develop strategies to enhance tourist satisfaction. The findings suggest that there are significant differences in the relationships between specific dimensions and that gender and culture moderate or impact these relationships. 2024-02-14T10:23:39Z 2024-02-14T10:23:39Z 2024-02-03 journal article Víctor Calderón-Fajardo, Rafael Anaya-Sánchez, Sebastian Molinillo, Understanding destination brand experience through data mining and machine learning, Journal of Destination Marketing & Management, Volume 31, 2024, 100862, ISSN 2212-571X, https://doi.org/10.1016/j.jdmm.2024.100862 https://hdl.handle.net/10630/30440 10.1016/j.jdmm.2024.100862 eng http://creativecommons.org/licenses/by-nc-nd/4.0/ open access Attribution-NonCommercial-NoDerivatives 4.0 Internacional Elsevier