Mining affective experience for a kansei design study on a recliner.

As the technical performance of products progresses, it is becoming more important to design products that satisfy customers' affective experiences. Hence, many studies about Kansei engineering or Kansei design have been conducted to develop products that can satisfy customers' affective experiences. In the Kansei design method, it is important to select affective variables related to the design elements of the product in order to accurately grasp the emotions of customers. Therefore, this study seeks to develop an affective variable extraction methodology that can reflect users' implicit needs effectively and efficiently. In this study, users' affective variables were extracted from online reviews and classified using a self-organizing map (SOM). For verification, the study selected the Amazon e-commerce service and performed a product experiment on recliners. The experimental results show that the most frequently used affective variable in the use of recliners is ‘comfort’, which is related to various affective variables. In addition, 15 clusters for affective experiences of recliners extracted from Amazon.com were classified through the SOM. The findings suggest that text mining techniques and the SOM can be used to gather and analyze customers' affective experiences effectively and efficiently. The results of this study can also enhance an understanding of customers' emotions regarding recliners.