A User Review Analysis Tool Empowering Iterative Product Design

Understanding user feedback and improving accordingly is an essential phase in iterative product design. User reviews provide sufficient and various user feedback about general experience, specific features, and bug reports. While the traditional manual analysis may yield some insights, it is often constrained by limited sample sizes and potential biases. This paper introduces a user review analysis tool equipped with a graphical user interface, based on multiple machine learning and natural language processing techniques. This tool is designed to aid developers in rapidly acquiring a qualitative understanding of user experiences regarding product features and topics, by illustrating user attitude evolution and summarizing diverse user opinions. The tool’s performance is evaluated with datasets from three different applications, each containing 50000 reviews. It was able to identify major features and topics and illustrated the evolution of user attitudes. Its effectiveness in summarizing diverse user opinions was also highlighted. The user review analysis tool can enable developers to efficiently process and interpret large volumes of user feedback, thereby significantly enhancing the iterative design process with data-driven insights.