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In this study, we through the data analysis to obtain a list of user demand and priorities to provide different opinions and recommendations for product design and marketing. The study is divided into two parts, one of which is to build a usefulness model in product perspective, and then use product community reviews as an evaluation corpus and experimental data set. The other is to propose a new feature-sentiment extraction method based on the research status of implicit features, and then select the basic algorithm of random forest as the classification model to combine text mining technology with KANO model for user demand research. Based on this, a new user demand priority calculation method is proposed. According to the study, the user's priority list provides realtime and accurate recommendations for product design and improvement, which is more practical in today's information overload era.

Product Perspective: A Study of User Demand Priorities in Online Community Reviews