Business Analysis: User Attitude Evaluation and Prediction Based on Hotel User Reviews and Text Mining

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.