Listen to the guests: Text-mining Airbnb reviews to explore indoor environmental quality

Abstract

Occupant comfort and satisfaction in residential buildings is often subpar, yet traditional post-occupancy evaluation surveys are time-consuming and difficult to achieve large samples. Moreover, quantitative results do not necessarily provide insights about how to improve indoor environmental quality (IEQ). To address these limitations, this paper proposes a novel method to develop new knowledge about occupant comfort and satisfaction: text-mining of public guest reviews in temporary accommodations. Using a set of 1.35-million Canadian Airbnb reviews, a methodology is developed and demonstrated to obtain top reported causes for IEQ complaints, assess seasonable trends of IEQ issues, and quantify the frequency of multi-domain IEQ complaints. The results indicate that five percent of all reviews complained of IEQ issues, while a quarter of a percent complained of multiple forms of IEQ. Reviews with IEQ complaints had a statistically significantly worse overall sentiment score, suggesting significant importance of IEQ on overall guest satisfaction. Overall, the method yielded new quantitative and qualitative knowledge about IEQ in guest homes, but the developed text-mining methods have some limitations, such as failing to correctly interpret idioms and distinguishing comfort-related words that have multiple meanings.