Mining online reviews with a Kansei-integrated Kano model for innovative product design

Abstract

ABSTRACT Optimising affective design based on customer needs help to earn competitive advantages. However, affective design has been studied qualitatively and the value of online opinions providing affective design ideas has not been exploited deeply. To fill this gap, a framework is proposed to reveal customer affective needs from a perspective of the Kansei-integrated Kano model. Firstly, inspired by Kansei Engineering, customer affective emotions are extracted contextually from online reviews. Next, related product features are positioned based on syntactic relations and a clustering algorithm. Enlightened by the Kano model, product features are prioritised based on affective emotions to show their importance on customer satisfaction. Finally, experiments with practical data are illustrated to evaluate the effectiveness in innovative product design. Take smartphone for example and camera, material, aesthetic design, safety, service, physical interface, and price are found to be attractive features in this study. Additionally, product reviews are transferred into a structured format by analysing affective emotions and corresponding features. It provides a straightforward perspective on affective needs, which facilitate new product design by including innovative design ideas and the proposed KE-integrated Kano model helps to capture customer affective needs and give inspirable insights for affective design from the viewpoint of companies.