Fonte: Revista Encontros Científicos - Tourism & Management Studies; Vol 19, No 2 (2023); 21-37
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Resumo: 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 hospitalityservices.
