Abstract
Innovative product features may gain higher brand reputation with lower cost for companies. Besides functional features, products having differential advantages on aesthetic design are acknowledged to be attractive in the market. As a result, exploring customer affective needs plays a critical role in product design innovation. In this paper, a hybrid method is proposed to reveal and classify customer affective needs from online opinions, including customer affective emotions and related product features. Firstly, inspired by Kansei engineering (KE), a knowledge-based method is presented to extract customer affective emotions. Then, enlightened by Kano model which determines the priorities of product features based on their abilities in satisfying customers, affective features are automatically extracted and classified into Kano categories. Finally, empirical studies are investigated to evaluate the effectiveness of the proposed framework. Compared with others, this method achieves higher F-measure scores in different domains. It highlights that a data-driven integration of KE and Kano model brings novel ideas and advanced suggestions for product design and marketing management in the view of designers and managers.
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Acknowledgments
The work was supported by a Grant from the National Nature Science Foundation of China (project no. NSFC 71701019/G0114) and National Social Science Foundation of China (Grant. 19ATQ005).
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Jia, D., Jin, J. (2020). Mining Affective Needs from Online Opinions for Design Innovation. In: Wang, X., Zhang, R., Lee, YK., Sun, L., Moon, YS. (eds) Web and Big Data. APWeb-WAIM 2020. Lecture Notes in Computer Science(), vol 12318. Springer, Cham. https://doi.org/10.1007/978-3-030-60290-1_25
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DOI: https://doi.org/10.1007/978-3-030-60290-1_25
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