Previous studies carried out customer surveys by questionnaires to collect data for analyzing consumer requirements. In recent years, a large and growing body of literature has investigated the extraction of customer requirements and preferences from online reviews. However, since customer requirements change dynamically over time, traditional studies failed to obtain the change data of customer requirements and opinions based on sentiments expressed in reviews. In this paper, a new method for dynamically mining user requirements is proposed, which is used to analyze the changing behavior of product attributes and improve product design. Dynamic mining differs from the traditional need acquisition mainly in three aspects: (1) it involves dynamically mining user requirements over time (2) it adds changes in manufacturers’ opinions to the analysis (3) it allows for product improvement strategies based on the changing behavior of product attributes. First, text mining is adopted to collect customer and manufacturer review data for different time periods and extract product attributes. A Natural Language Processing tool is used to measure the importance weight and sentiment score of product attributes. Second, an approach for dynamically mining user requirements is introduced to classify product attributes and analyze the changes of attribute data in three categories over time. Finally, an improvement strategy for next-generation product design is developed based on the changing behavior of attributes. Moreover, a case study on vehicles based on online reviews was conducted to illustrate the proposed methodology. Our research suggests that the proposed approach can accurately mine customer requirements and lead to successful product improvement strategies for next-generation products.

先前的研究通过问卷调查进行了客户调查，以收集用于分析消费者需求的数据。近年来，越来越多的文献研究了从在线评论中提取客户需求和偏好的方法。但是，由于客户需求随时间动态变化，因此传统研究未能基于评论中表达的情感来获取客户需求和意见的变化数据。本文提出了一种动态挖掘用户需求的新方法，用于分析产品属性的变化行为并改进产品设计。动态挖掘与传统需求获取的不同之处主要在于三个方面：（1）随时间推移动态挖掘用户需求（2）在分析中增加制造商意见的变化（3）根据产品属性的变化行为制定产品改进策略。首先，采用文本挖掘来收集不同时间段的客户和制造商评论数据并提取产品属性。使用自然语言处理工具来衡量产品属性的重要性和情感分数。其次，引入了一种动态挖掘用户需求的方法，以对产品属性进行分类并分析随时间变化的三类属性数据的变化。最后，根据属性的变化行为，开发了用于下一代产品设计的改进策略。此外，进行了基于在线评论的车辆案例研究，以说明所建议的方法。我们的研究表明，所提出的方法可以准确地挖掘客户的需求，并为下一代产品带来成功的产品改进策略。