从在线评论中挖掘用户期望随时间的变化
Mining Changes in User Expectation Over Time From Online Reviews
摘要：
客户可以随时在线评论。有了在线评论的时间戳，它们可以被视为一种信息流。有了这个特性，设计师可以捕捉客户反馈的变化，以帮助建立产品改进策略。在这里，我们提出了一种方法来捕捉用户的期望变化的基础上的在线评论的两代产品的产品启示。首先，该方法使用基于规则的自然语言处理方法从评论文本中自动识别和结构化产品启示。然后，受卡诺模型的启发，将产品属性的偏好分为五类，联合分析被用来定量分类的结构化启示。最后，通过对连续两代产品的在线评论进行联合分析，可以发现用户期望的变化。基于Kindle电子阅读器在线评论的案例研究表明，设计师可以使用我们提出的方法来评估他们的产品改进策略，为以前的产品和开发新的产品改进策略，为未来的产品。amazon.com
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
