Affective design using machine learning: a survey and its prospect of conjoining big data

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.

KEYWORDS:

• Affective design
• affective smart systems
• big data
• Kansei engineering
• machine learning
• new product development
• social media

Acknowledgments

The work described in this paper was supported by a grant from The Hong Kong Polytechnic University (Project No. G-UADL).

Disclosure statement

No potential conflict of interest was reported by the authors.

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.