Perception of customer satisfaction and complaints based on BERTopic and interpretable machine learning: evidence from hotels in Xi’an

This study aims to investigate the topics of customer satisfaction and complaints and explore their role in review sentiment prediction by using deep learning and interpretable machine learning methods. Specifically, this study first uses a BERT-based method for topic clustering and embedding, a factor analysis for topic feature extraction and dimensionality reduction and an oversampling method for the imbalance problem. Further, five types of machine learning methods are utilised to forecast review sentiment. To improve the interpretability of the machine learning model, we employ an interpretable framework to explore the role of topic features in review sentiment prediction. The results indicate that customers' satisfaction mainly focuses on perfect conference facilities and delicious Chinese cuisine, while customers mainly complain about the quality of service, the attitude of staff and the accessibility of the hotel. In addition, 'air quality and traffic' is the most important factor influencing the sentiment in reviews. These findings suggest that hotel managers should focus more on the accommodation experience of customers rather than the value-added experience.