A dynamic customer requirement mining method for continuous product improvement

Abstract The key to successful product development is better understanding of customer requirements and efficiently identifying the attributes. In recent years, a growing number researchers have studied mining preferences from online reviews. However, since often change dynamically on multi-generation products, most existing studies failed discover correlations between satisfaction continuous improvement. this work, we propose novel dynamic requirement method analyze changes attributes based sentiment attention expressed in reviews, aiming meet provide direction content future Specifically, divided into three parts. Firstly, text adopted collect review data products identify Secondly, scores are calculated with natural language processing tool, further integrated corresponding scores. Finally, improvement for next-generation determined changing addition, case study phone reviews was conducted illustrate effectiveness practicality proposed methodology. Our research completes field analysis provides new approach which can help enterprises accurately understand improve efficiency