Improved Service Quality Assessment Through the Integration of Principal Component Analysis and Support Vector Machines

This article presents a novel model for the improvement of service quality assessment in the context of big data analysis. With the development of globalization and information technology, big data analysis has become an indispensable tool in various markets. The quality of services provided by companies has a direct impact on their image and customer satisfaction. The proposed model combines Support Vector Machine (SVM) with Principal Component Analysis (PCA) to automatically analyze and categorize sentiment from massive amounts of user evaluation data. This allows for the accurate identification of customer satisfaction and dissatisfaction towards services, providing suggestions for improving company services. Experimental results demonstrate that the model can effectively enhance service quality and customer satisfaction, thereby increasing the competitiveness of businesses in various industries. This research provides valuable insights for the development of big data analysis technology, offering new approaches and methods for future research.