Cross-language hotel review sentiment analysis via multi-agent federated learning with heterogeneous graph attention networks.

This paper presents an integrated framework for cross-language hotel review sentiment analysis that combines multi-agent federated learning with heterogeneous graph attention networks to address privacy preservation and multilingual data processing challenges in hospitality reputation management. Our system enables collaborative model training across distributed review platforms while maintaining data locality requirements and achieving improved cross-language sentiment classification performance. Beyond sentiment analysis, we developed dynamic reputation management and fake review detection capabilities that enable proactive intervention strategies for hospitality businesses. The heterogeneous graph architecture captures complex relationships between multilingual textual content, user behaviors, temporal patterns, and service attributes through specialized attention mechanisms. Experimental evaluation on a comprehensive multilingual dataset of 154,680 reviews across four languages demonstrates 89.7 ± 0.007 accuracy in sentiment classification with 0.925 privacy preservation score (Table 6), representing 2.6% point improvement over the strongest baseline XLM-RoBERTa large (87.1 ± 0.008 accuracy, paired t-test p = 0.002). The dynamic reputation management component provides real-time monitoring capabilities with early warning detection, achieving 93.4 ± 0.012 fake review identification accuracy and 66.2% reduction in response time compared to traditional centralized approaches (Table 9). The system offers practical applications for hospitality businesses seeking proactive reputation management while ensuring compliance with international data privacy regulations including GDPR and CCPA.