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.

中文翻译：

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使用机器学习的情感设计：大数据联合调查及其前景

摘要 购买新产品的客户满意度是当今竞争激烈的市场中需要解决的一个重要问题。具有良好情感设计的产品会激发消费者的情感去购买该产品。情感设计通常涉及复杂的多维问题，用于建模和最大化客户的情感满意度。机器学习通常用于建模和最大化情感满意度，因为当与模式相关的数值数据可用时，它可以有效地对非线性模式进行建模。这篇评论文章对使用传统调查数据和现代大数据这两个数据流时情感设计的常用机器学习方法进行了调查。首先为传统调查数据提供了机器学习技术的分类。讨论了机器学习技术的局限性和优势。由于与情感设计相关的大数据可以从社交媒体中获取，因此讨论了使用大数据来增强情感设计的前景和挑战，迄今为止，在这方面的研究还很有限。这篇评论文章对那些使用机器学习技术进行情感设计的人很有用，也为那些有兴趣将大数据和机器学习技术用于情感设计的研究人员提供了指导。