Examining Airbnb guest satisfaction tendencies: a text mining approach

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.

KEYWORDS:

• Airbnb
• online reviews
• topic modelling
• sentiment analysis
• satisfaction
• hospitality

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by Fundação para a Ciência e Tecnologia [grant number UID/ECO/04007/2022], [grant number UIDB/50021/2020].