In the post-pandemic era, the hotel industry plays a crucial role in economic recovery, with consumer sentiment increasingly influencing market trends. This study utilizes advanced natural language processing (NLP) and the BERT model to analyze user reviews, extracting insights into customer satisfaction and guiding service improvements. By transforming reviews into feature vectors, the BERT model accurately classifies emotions, uncovering patterns of satisfaction and dissatisfaction. This approach provides valuable data for hotel management, helping them refine service offerings and improve customer experiences. From a financial perspective, understanding sentiment is vital for predicting market performance, as shifts in consumer sentiment often correlate with stock prices and overall industry performance. Additionally, the study addresses data imbalance in sentiment analysis, employing techniques like oversampling and undersampling to enhance model robustness. The results offer actionable insights not only for the hotel industry but also for financial analysts, aiding in market forecasts and investment decisions. This research highlights the potential of sentiment analysis to drive business growth, improve financial outcomes, and enhance competitive advantage in the dynamic tourism and hospitality sectors, thereby contributing to the broader economic landscape.

翻译：在后疫情时代，酒店业对经济复苏起着至关重要的作用，消费者情绪日益影响市场趋势。本研究利用先进的自然语言处理技术和BERT模型分析用户评论，提取有关客户满意度的见解，并指导服务改进。通过将评论转化为特征向量，BERT模型能够准确分类情感，揭示满意与不满意的模式。该方法为酒店管理提供了宝贵的数据，帮助其优化服务项目并提升客户体验。从金融视角来看，理解情绪对于预测市场表现至关重要，因为消费者情绪的变化往往与股价及整体行业表现相关。此外，本研究还解决了情感分析中的数据不平衡问题，采用了过采样与欠采样等技术以增强模型的鲁棒性。研究结果不仅为酒店业提供了可操作的见解，也为金融分析师提供了帮助，辅助市场预测与投资决策。本研究凸显了情感分析在推动业务增长、改善财务成果以及增强动态旅游与酒店业竞争优势方面的潜力，从而为更广泛的经济格局做出贡献。