The Impact of Social Media Data Mining on Enhancing Customer Satisfaction in Macaus Hotel Industry

In the digital era, social media platforms have become indispensable tools for communication, marketing, and consumer engagement, especially in the travel and hospitality industry. This study examines how social media data mining techniques, including text mining and sentiment analysis, can be applied to uncover the drivers of customer satisfaction within Macaus competitive hotel sector. Leveraging theoretical frameworks such as SERVQUAL and the Kano model, the research identifies key factors influencing guest satisfactionsuch as cleanliness, service speed, pricing, and environmentand demonstrates how real-time social media insights enable hotels to promptly address service shortcomings and tailor their offerings. This proactive approach not only enhances customer satisfaction but also fosters loyalty and positive online word-of-mouth, crucial for sustaining competitive advantage. The study also highlights the potential of social media analytics to detect emerging trends, supporting hotels in adapting to evolving customer needs. However, limitations include reliance on secondary data and the specific regional focus on Macau, which may affect the broader applicability of findings. Future research is encouraged to incorporate mixed-methods and cross-regional comparative studies to validate and deepen the insights from social media data mining in hospitality management.