What do Airbnb users care about? An analysis of online review comments

• Published in: International journal of hospitality management Vol. 76; pp. 58 - 70
• Main Authors: Cheng, Mingming, Jin, Xin
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.01.2019
• Subjects: Airbnb; Big data; Sentiment analysis; Unsupervised learning; User experience; Sentiment analysis; User experience; Big data; Airbnb; Unsupervised learning
• ISSN: 0278-4319; 1873-4693
• DOI: 10.1016/j.ijhm.2018.04.004

•Three key attributes of an Airbnb experience: location, amenities and host.•Airbnb users tend to evaluate Airbnb experience based on a frame of reference derived from past hotel stays.•Airbnb users tend to use the names of the owners in their comments carrying a personal touch.•Airbnb challenges the notion of professionalism in hospitality.•Sense of privacy and safety is valued by the guests. This study investigates the attributes that influence Airbnb users’ experiences by analysing a “big data” set of online review comments through the process of text mining and sentiment analysis. Findings reveal that Airbnb users tend to evaluate their experience based on a frame of reference derived from past hotel stays. Three key attributes identified in the data include ‘location’, ‘amenities’ and ‘host’. Surprisingly, ‘price’ is not identified as a key influencer. The analysis suggests a positivity bias in Airbnb users’ comments while negative sentiments are mostly caused by ‘noise’. This research offers an alternative approach and more coherent understanding of the Airbnb experience. Methodologically, it contributes by illustrating how big data can be used and visually interpreted in tourism and hospitality studies.