Examining transaction-specific satisfaction and trust in Airbnb and hotels. An application of BERTopic and Zero-shot text classification

• Rey-Moreno, Manuel ^[1] ; Sánchez-Franco, Manuel Jesús ^[1] ; Rey-Tienda, María De la Sierra ^[2]
  1. [1] Universidad de Sevilla

     Universidad de Sevilla

     Sevilla, España

     
  2. [2] "Loyola University of Andalusia"
• Localización: Tourism & Management Studies, ISSN-e 2182-8466, ISSN 2182-8458, Vol. 19, Nº. 2, 2023, págs. 21-37
• Idioma: inglés
• DOI: 10.18089/tms.2023.190202
• Enlaces
  • Texto completo (pdf)
• Resumen
  • español

    El artículo analiza, desde una aproximación metodológica, la aplicación de la minería de datos al contenido generado por los usuarios en plataformas de infomediación y redes sociales de servicios de alojamiento turístico. El objetivo del paper es presentar un algoritmo que permita identificar los atributos más influyentes de este servicio en la satisfacción y confianza del huésped. Nuestro estudio procesa datos presentados en un lenguaje natural y desestructurado relativos a las estancias en hoteles y alojamientos Airbnb (la base de datos final fue de 12236 opiniones sobre servicios Airbnb y 12200 sobre hoteles, recogidas desde comienzos de 2018 hasta 25.09.2021). Entre los resultados obtenidos se encuentra un algoritmo computacional que utiliza BERTopic para identificar temas latentes en las narrativas. En segundo lugar, nuestro análisis aplica Zeroshot para clasificar las revisiones de los invitados en etiquetas relacionadas con su satisfacción y confianza. En tercer lugar, ejecutamos un Análisis de Componentes Principales para investigar las relaciones de suficiencia entre los tópicos extraídos y las etiquetas relacionadas con la satisfacción y confianza del cliente. Se añade al conocimiento sobre economía compartida nuevas perspectivas para el desarrollo de políticas de marketing y una mejor comprensión de los servicios de alojamiento.

  • English

    With a methodological approach, this article explores the application ofdata mining to the user-generated content of tourist accommodationon infomediation platforms and social networks. Its objective is topresent an algorithm that allows the identification of servicecharacteristics relevant to guest satisfaction and trust. Our studyprocesses unstructured, natural-language data about Airbnb and hotelstays (the final dataset was 12,236 Airbnb sentences and 12,200 hotelsentences from 2018 until September 25 2021). Among the results is acomputational algorithm that uses BERTopic to identify latent themes(or topics) in the narratives. Secondly, our analysis applies a Zero-shotclassification approach for classifying guest reviews into labels relatedto guests' satisfaction and trust. Thirdly, we execute a PrincipalComponent Analysis to investigate the sufficiency relationshipsbetween extracted topics, customer satisfaction, and trust-basedlabels. To sum up, and as practical implications, our study adds to theknowledge about the sharing economy by providing insights fordeveloping marketing policies and a better understanding of hospitality services.

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