Dynamic Identification of Hotel Service Experience Quality Based on Semantic Mining of Online Reviews

Abstract

This study develops a dynamic framework for identifying hotel service experience quality through semantic mining of online reviews. Ctrip reviews from 260 hotels in Shanghai, Beijing, Guangzhou, Chengdu, and Hangzhou between 2021 and 2024 were cleaned into 156,000 valid reviews and 10,842 hotel-month observations. The framework combines a service-experience ontology, dependency-based aspect extraction, SKEP sentiment scoring, complaint intensity, change-point detection, and Gaussian hidden Markov modeling across eight service dimensions. Results show that semantic indicators detect deterioration earlier than numerical ratings, especially in cleanliness, facility reliability, and problem recovery, where complaint intensity rose 18%–27% above the sample mean during risk periods. The model classifies four quality states and achieves an F1-score of 0.829 and AUC of 0.903 for predicting next-month service decline, providing a reproducible method for dynamic hotel quality monitoring.