Customers post online reviews at any time. With the timestamp of online reviews, they can be regarded as a flow of information. With this characteristic, designers can capture the changes in customer feedback to help set up product improvement strategies. Here we propose an approach for capturing changes of user expectation on product affordances based on the online reviews for two generations of products. First, the approach uses a rule-based natural language processing method to automatically identify and structure product affordances from review text. Then, inspired by the Kano model which classifies preferences of product attributes in five categories, conjoint analysis is used to quantitatively categorize the structured affordances. Finally, changes of user expectation can be found by applying the conjoint analysis on the online reviews posted for two successive generations of products. A case study based on the online reviews of Kindle e-readers downloaded from amazon.com shows that designers can use our proposed approach to evaluate their product improvement strategies for previous products and develop new product improvement strategies for future products.

翻译：客户随时张贴在线审查 。 随着在线审查的时间戳, 他们可以被视为一种信息流 。 有了这一特点, 设计师可以捕捉客户反馈的变化, 以帮助制定产品改进战略 。 我们在这里根据对两代产品的在线审查, 提出一种方法来捕捉用户对产品价格的预期变化 。 首先, 该方法使用基于规则的自然语言处理方法, 自动识别和构建来自审查文本的产品价格 。 然后, 在将产品属性的偏好分为五类的卡诺模式的启发下, 联合分析用于对结构化的支付能力进行定量分类 。 最后, 通过对连续两代产品的在线审查应用联合分析, 可以发现用户期望的变化 。 基于从Amazon下载的Kindle电子阅读器的在线审查的案例研究表明, 设计师可以使用我们提出的方法, 来评价其产品改进战略, 并为未来产品制定新的产品改进战略 。