Satisfaction Evaluation System of Macau Attractions Based on Online Evaluation Data

Abstract

Online reviews are a valuable source for understanding tourist satisfaction and their emotional tendencies towards attractions. However, there is a need to improve the quality screening of reviews before conducting sentiment analysis. This paper focuses on the important attractions in Macao and utilizes data collected from Ctrip.com to establish an evaluation system. The system scores and ranks the validity of online reviews, and various machine learning methods are examined to automate the screening process. The study finds that XGBoost + stacking is the most effective method, with the upper quartile serving as the threshold for review selection. By employing text sentiment analysis technology, the study evaluates visitor satisfaction for each attraction using quality reviews. This approach contributes to the development of a more comprehensive satisfaction evaluation system and offers a fresh analytical perspective for studying Macau tourism.