Affective design using machine learning: a survey and its prospect of conjoining big data. Issue 7 (2nd July 2020)

• Record Type: Journal Article
• Authors: Chan, Kit Yan; Kwong, C.K.; Wongthongtham, Pornpit; Jiang, Huimin; Fung, Chris K.Y.; Abu-Salih, Bilal; Liu, Zhixin; Wong, T.C.; Jain, Pratima
• Is Part Of: International journal of computer integrated manufacturing. Volume 33:Issue 7(2020)
• Page Start: 645
• Page End: 669
• Publication Date: 2020-07-02
• Subjects: Affective design -- affective smart systems -- big data -- Kansei engineering -- machine learning -- new product development -- social media; Computer integrated manufacturing systems -- Periodicals; 670.427
• DOI: 10.1080/0951192X.2018.1526412
• Languages: English
• ISSNs: 0951-192X

Abstract

ABSTRACT: Customer satisfaction in purchasing new products is an important issue that needs to be addressed in today's competitive markets. A product with good affective design excites consumer emotional feelings to buy the product. Affective design often involves complex and multi-dimensional problems for modelling and maximising affective satisfaction of customers. Machine learning is commonly used to model and maximise the affective satisfaction, since it is effective in modelling nonlinear patterns when numerical data relevant to the patterns is available. This review article presents a survey of commonly used machine learning approaches for affective design when two data streams, traditional survey data and modern big data, are used. A classification of machine learning technologies is first provided for traditional survey data. The limitations and advantages of machine learning technologies are discussed. Since big data related to affective design can be captured from social media, the prospects and challenges in using big data are discussed to enhance affective design, in which limited research has so far been attempted. This review article is useful for those who use machine learning technologies for affective design, and also provides guidelines for researchers who are interested in incorporating big data and machine learning technologies for affective design.