Examining transaction-specific satisfaction and trust in Airbnb and hotels. An application of BERTopic and Zero-shot text classification
Keywords:
Airbnb, Hotels, Satisfaction, Trust, BERT, Zero-shotAbstract
With a methodological approach, this article explores the application of
data mining to the user-generated content of tourist accommodation
on infomediation platforms and social networks. Its objective is to
present an algorithm that allows the identification of service
characteristics relevant to guest satisfaction and trust. Our study
processes unstructured, natural-language data about Airbnb and hotel
stays (the final dataset was 12,236 Airbnb sentences and 12,200 hotel
sentences from 2018 until September 25 2021). Among the results is a
computational algorithm that uses BERTopic to identify latent themes
(or topics) in the narratives. Secondly, our analysis applies a Zero-shot
classification approach for classifying guest reviews into labels related
to guests' satisfaction and trust. Thirdly, we execute a Principal
Component Analysis to investigate the sufficiency relationships
between extracted topics, customer satisfaction, and trust-based
labels. To sum up, and as practical implications, our study adds to the
knowledge about the sharing economy by providing insights for
developing marketing policies and a better understanding of hospitality
services.
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