Research on the design method of extracting optimal kansei vocabulary

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Abstract

In the relevant researches on Kansei Engineering, the Kansei Vocabulary extraction has a vital significance. In previous time, the Kansei words are selected by experts’ focus interviews or customers’ giving marks. Such kind of method is easy, but it is difficult to explore the customers’ inner feeling, which seems to be so hasty. In this research, a method of selecting optimal Kansei Vocabulary is proposed to assist the designers establish the high correlation degree’s emotion cognition of customers. The factor analysis is used to classify the Kansei semantic style. Using the Fuzzy Analytic Hierarchy Process to make comparisons of each two specific Kansei words can get the final weight order. Through this method in the minicar’s case study, the modern factors’ “concise”, “smooth” words are defined as the words which can most arouse the customers’ emotional resonance. The research proves that the design method of extracting the optimal Kansei Vocabulary is the most effective one. Meanwhile, it can be applied into the modeling design of other industrial products.

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APA

Kang, X., Yang, M., Wu, Y., & Yuan, H. (2017). Research on the design method of extracting optimal kansei vocabulary. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10273 LNCS, pp. 194–207). Springer Verlag. https://doi.org/10.1007/978-3-319-58521-5_15

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