ABSTRACT Affective design is an important aspect of new product development that can enhance customer satisfaction of new products. Previous studies generally conducted customer surveys based on questionnaires and interviews to collect customers’ views and preferences of affective design of products. However, the process could be time-consuming and the survey data does not contain much sentiment expression. Presently, a large number of online customer reviews on products can be found on various websites that contain rich information of customer opinions and expectations. However, the generation of useful information based on online customer reviews for affective design has not been addressed in previous studies. In this paper, a methodology for generating association rules for supporting affective design based on online customer reviews is proposed which mainly involves opinion mining of affective dimensions from online customer reviews and association rule mining based on multi-objective particle swarm optimisation (PSO). Opinion mining is adopted to analyze online reviews and conduct sentiment analysis for affective dimensions. Based on the mined information and morphological analysis of products, a multi-objective PSO approach is proposed to generate association rules that depict the relationships between affective dimensions and design attributes. A case study was conducted to illustrate the proposed methodology.

中文翻译：

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基于在线顾客评论的情感设计关联规则挖掘多目标PSO方法

摘要 情感设计是新产品开发的一个重要方面，可以提高客户对新产品的满意度。以往的研究一般基于问卷和访谈进行客户调查，以收集客户对产品情感设计的看法和偏好。然而，这个过程可能很耗时，而且调查数据不包含太多的情感表达。目前，在各种网站上都可以找到大量关于产品的在线客户评论，其中包含丰富的客户意见和期望信息。然而，以前的研究没有解决基于在线客户评论为情感设计生成有用信息的问题。在本文中，提出了一种基于在线客户评论生成支持情感设计的关联规则的方法，主要包括在线客户评论情感维度的意见挖掘和基于多目标粒子群优化（PSO）的关联规则挖掘。采用意见挖掘对在线评论进行分析，对情感维度进行情感分析。基于产品的挖掘信息和形态分析，提出了一种多目标PSO方法来生成描述情感维度和设计属性之间关系的关联规则。进行了一个案例研究来说明所提出的方法。