Gauging Airbnb review sentiments and critical key-topics by small area estimation

Abstract

In literature, several researchers have discovered that the reviews written about Airbnb accommodation tend to be extremely positive than those published on other famous platforms, consequently, many negative experiences remain untracked. Leaving negative experiences underrepresented hampers hosts’ ability to improve their services. To overcome this gap, we employ Small Area Estimation to quantify negative sentiment in Airbnb reviews and the relative critical topics that characterize them. Our methodology involves a two-step process: first, we employ sentiment analysis and topic modeling to identify negative sentiment and critical issues, followed by the application of a mixed effect random forest model to provide a granular analysis of Airbnb reviews in small sub-populations in the context of small area estimation. We focus on domains of the city of Rome defined by geographical areas and the presence of hosts and Superhosts. Our findings reveal nuanced sentiment variations and critical topic proportions that traditional methods often overlook.