Practice on Framework for Product Quality Analysis Based on User Feedback Data

Abstract Online products generate vast amounts of user feedback data, which has become crucial for companies to improve product quality and customer satisfaction. This paper proposes the FPQA-UFD (framework to analyze product quality based on user feedback data) using data mining algorithms, natural language processing, multi-classification methods, and statistical analysis, providing detailed data support for product development teams' decision-making. The framework effectively extracts information from user feedback, accurately dividing 305,311 user feedback data into 44 effective topics and extracting explanatory keywords. A multi-classification experiment achieved a classification accuracy and recall rate of 83%. This study offers valuable insights for businesses and academia to enhance decision-making and software development through user feedback analysis.