Key attributes of Airbnb experience: Text-mining and sentiment analysis

Publication Year:
2018
Usage 70
Abstract Views 57
Downloads 13
Repository URL:
https://scholarworks.umass.edu/ttra/2018/Academic_Papers_Oral/23
Author(s):
Cheng, Mingming, Dr; Jin, Xin, Dr
artifact description
Using a “big data” set of online review comments from Airbnb users, this research investigates the attributes forming Airbnb user experience. Both text mining and sentiment analysis were performed to analyze 170,124 review comments from Airbnb users in Sydney, Australia. Three key attributes are identified including location, amenities and host. Price does not emerge as a key attribute. While the analysis suggests a positivity bias in Airbnb users’ comments, noise receives a large number of negative sentiments. Findings also reveal that Airbnb users tend to evaluate Airbnb experience based on a frame of reference derived from past hotel stays. This research offers a more detailed understanding of the sharing economy experience in the case of Airbnb and methodologically contributes to the on-going reconfiguration of social media data by using big data techniques.