Investigating the determinants of homestay satisfaction on Airbnb using multiple techniques

Abstract

Abstract Peer-to-peer accommodation has gained prominence in the sharing economy and e-commerce sectors, with big data playing a crucial role in understanding customer preferences and evaluating homestay satisfaction. This study proposes a novel methodology that integrates Natural Language Processing (NLP) techniques, a Random Forest model, and Geographic Information System (GIS) functionalities to quantify the complex relationship between homestay satisfaction and diverse customer preferences. Notably, this study addresses the positive bias inherent in listing scores by segmenting homestays into three categories (satisfactory, moderate, and dissatisfactory) based on sentiment analysis from online reviews. Furthermore, this study not only identifies eight key determinants of homestay satisfaction but also unveils the nonlinear relationships and interactions between them. More significantly, we identify specific threshold values for geographic determinants, offering actionable recommendations for homestay planning and layout. These findings provide valuable insights that can be leveraged to improve homestay experiences and promote the sustainable development of urban homestays.

Keywords

• Airbnb
• Homestay satisfaction
• Random Forest
• Sentiment analysis