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

Chan Kit Yan; Kwong C. K.; Wongthongtham Pornpit; Jiang Huimin; Fung Chris K. Y.; Abu-Salih Bilal; Liu Zhixin; Wong T. C.; Jain Pratima

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

机译：利用机器学习的情感设计：调查及其联合大数据的前景

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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.

机译：购买新产品的客户满意度是当今竞争市场中需要解决的重要问题。一种具有良好情感设计的产品激发了消费者的情感情绪来购买产品。情感设计往往涉及复杂和多维问题，用于建模和最大化客户的情感满意度。机器学习通常用于模拟并最大化情感满足，因为当与模式相关的数值数据可用时，它有效地建模非线性模式。该审查文章在使用两个数据流，传统调查数据和现代大数据时，对情感设计常用的机器学习方法进行了调查。首先为传统的调查数据提供机器学习技术的分类。讨论了机器学习技术的局限性和优点。由于可以从社交媒体中捕获与情感设计相关的大数据，因此讨论了使用大数据的前景和挑战以增强情感设计，其中迄今为止已经尝试了有限的研究。该审查文章对使用机器学习技术的人们对情感设计的人有用，并且还为有兴趣纳入情感设计的大数据和机器学习技术的研究人员提供了指导方针。

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来源
《International Journal of Computer Integrated Manufacturing》 |2020年第9期|共25页
作者
Chan Kit Yan; Kwong C. K.; Wongthongtham Pornpit; Jiang Huimin; Fung Chris K. Y.; Abu-Salih Bilal; Liu Zhixin; Wong T. C.; Jain Pratima;
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作者单位

Curtin Univ Sch Elect Engn Comp &

Math Sci Perth WA Australia;

Hong Kong Polytech Univ Dept Ind &

Syst Engn Hong Kong Peoples R China;

Curtin Univ Sustainabil Policy Inst Perth WA Australia;

Hong Kong Polytech Univ Dept Ind &

Syst Engn Hong Kong Peoples R China;

Hong Kong Polytech Univ Dept Ind &

Syst Engn Hong Kong Peoples R China;

Curtin Univ Sch Informat Syst Perth WA Australia;

Yanshan Univ Inst Elect Engn Qinhuangdao Hebei Peoples R China;

Univ Strathclyde Dept Design Manufacture &

Engn Management Glasgow Lanark Scotland;

Curtin Univ Sch Informat Syst Perth WA Australia;

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原文格式 PDF
正文语种 eng
中图分类计算机集成制造;
关键词
Affective design; affective smart systems; big data; Kansei engineering; machine learning; new product development; social media;

机译：情感设计;情感智能系统;大数据;Kansei Engineering;机器学习;新产品开发;社交媒体;

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

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