Accommodation Descriptions that Influence Airbnb Occupancy Rate Using Ontology

Abstract

The purpose of this study is to determine the occupancy rate influential model on the Airbnb platform by mapping accommodation descriptions to ontology. This research presented the model and developed an ontology of Bangkok travel information. It defined topics at the start of ontology processing to find the topic that influence on the occupancy rate the most. In this research, the outstanding characteristics of the studied city were identified, defined and categorized by creating the Bangkok Travel Information Ontology to determine the average occupancy rate that occurs when writing each accommodation description to prove whether the strengths are presented in writing or not and to be a tool to help renters on the online platform rent rooms. It can be applied to set a strategy for writing descriptions of various strengths according to ontology and presenting data analysis results through data visualization. The hidden topics described in the Bangkok Travel Ontology are important and affect the rate of room rental on Airbnb, which varies in percentage depending on the style of writing accommodation descriptions. In addition, there is a difference in average occupancy considering room type in different subjects and formats, with room type “entire home/apt” having a maximum booking average of 40% occupancy in ontology level 1 and 100% in ontology level 2. For room types, the hotel room type is a maximum booking average rental rate of 83.33% in ontology level 1 and 100% in the ontology level 2.