Scientific journal paper Q1
Examining Airbnb guest satisfaction tendencies: A text mining approach
Mariana Santos (Santos, M.); Ricardo Ribeiro (Ribeiro, R.); Fernando Batista (Batista, F.); Antónia Correia (Correia, A.);
Journal Title
Current Issues in Tourism
Year (definitive publication)
2022
Language
English
Country
United Kingdom
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Web of Science®

Times Cited: 10

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Scopus

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Times Cited: 14

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Abstract
Given Airbnb's changes since its inception and the dynamism of customer preferences, a study that sheds light on how customer satisfaction is evolving is relevant. An automated method is proposed for identifying these satisfaction tendencies at a large scale. This study follows a text mining approach to analyse 590,070 reviews posted between 2010 and 2019 on the Airbnb platform in Lisbon. Topic Modelling is employed in order to identify the main topics discussed in the reviews, and Sentiment Analysis to understand the topics that compose guest’s satisfaction in the context of Airbnb services. Three major topics are extracted from Airbnb reviews: ‘host’s service’, ‘physical aspects’, and ‘location’. Although a positivity bias in guest reviews is confirmed, the satisfaction level seems to be decreasing over the years. The results also reveal that ‘physical aspects’ is the predominant topic when considering the negative guest reviews. This research considers big data the base to create knowledge, data spanning over the years, offering consistency to the research.
Acknowledgements
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Keywords
Airbnb,Online reviews,Topic modelling,Sentiment analysis,Satisfaction,Hospitality
  • Computer and Information Sciences - Natural Sciences
  • Economics and Business - Social Sciences
  • Media and Communications - Social Sciences
  • Languages and Literature - Humanities
  • Other Humanities - Humanities
Funding Records
Funding Reference Funding Entity
UID/ECO/04007/2022 Fundação para a Ciência e a Tecnologia
UIDB/50021/2020 Fundação para a Ciência e a Tecnologia

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