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Are customer star ratings and sentiments aligned? A deep learning study of the customer service experience in tourism destinations

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Are customer star ratings and sentiments aligned? A deep learning study of the customer service experience in tourism destinations

dc.contributor.author	Bigné Alcañiz, J. Enrique
dc.contributor.author	Ruiz, Carla
dc.contributor.author	Pérez Cabañero, Carmen
dc.contributor.author	Cuenca, Antonio
dc.date.accessioned	2023-03-29T07:49:31Z
dc.date.available	2023-03-30T04:45:06Z
dc.date.issued	2023	es_ES
dc.identifier.citation	Bigne, E., Ruiz, C., Perez-Cabañero, C. et al. Are customer star ratings and sentiments aligned? A deep learning study of the customer service experience in tourism destinations. Serv Bus 17, 281–314 (2023)	es_ES
dc.identifier.uri	https://hdl.handle.net/10550/85955
dc.description.abstract	This study explores the consistency between star ratings and sentiments expressed in online reviews and how they relate to the different components of the customer experience. We combine deep learning applied to natural language processing, machine learning and artificial neural networks to identify how the positive and negative components of 20,954 online reviews posted on TripAdvisor about tourism attractions in Venice impact on their overall polarity and star ratings. Our findings showed that sentiment valence is aligned with star ratings. A cancel-out effect operates between the positive and negative sentiments linked to the service experience dimensions in mixed-neutral reviews.	es_ES
dc.language.iso	en	es_ES
dc.publisher	Springer	es_ES
dc.subject	sentiment analysis	es_ES
dc.subject	deep learning	es_ES
dc.subject	artificial neural networks	es_ES
dc.subject	tourism destination	es_ES
dc.subject	star rating	es_ES
dc.title	Are customer star ratings and sentiments aligned? A deep learning study of the customer service experience in tourism destinations	es_ES
dc.type	journal article	es_ES
dc.subject.unesco	UNESCO::CIENCIAS ECONÓMICAS	es_ES
dc.identifier.doi	10.1007/s11628-023-00524-0	es_ES
dc.identifier.idgrec	161805
dc.accrualmethod	S	es_ES
dc.embargo.terms	0 days	es_ES