Determination of the design attribute settings of a new product is essential for maximizing customer satisfaction. A model is necessary to illustrate the relation between the design attributes and dimensions of customer satisfaction such as product performance, affection and quality. The model is commonly developed based on customer survey data collected from questionnaires or interviews which require a long deployment time; hence the developed model cannot completely reflect the current marketplace. In this paper, a framework is proposed based on online reviews in which past and current customer opinions are included to develop the model. The proposed framework overcomes the limitation of the aforementioned approaches in which the developed models are not up-to-date. Indeed, the proposed framework develops models based on machine learning technologies, namely genetic programming, which has better generalization capabilities than classical approaches, and has higher transparency capabilities than implicit modelling approaches. To further enhance the prediction capability, committee member selection is proposed. The proposed selection method improves the currently used selection method which trains several models and only selects the best one. The proposed selection method generates a hybrid model which integrates the predictions of the generated models. Each prediction is weighted by how likely the prediction is agreed by others. The proposed framework is implemented on electric hair dryer design of which online reviews in amazon.com are used. Experimental results show that models with more accurate prediction capabilities can be generated by the proposed framework.

确定新产品的设计属性设置对于最大化客户满意度至关重要。必须有一个模型来说明设计属性和客户满意度的维度之间的关系，例如产品性能，情感和质量。该模型通常是基于从问卷或访谈中收集的客户调查数据而开发的，这些数据需要较长的部署时间；因此，开发的模型无法完全反映当前的市场。在本文中，提出了一个基于在线评论的框架，其中包含了过去和当前的客户意见以开发模型。所提出的框架克服了前述方法的局限性，其中所开发的模型不是最新的。确实，提出的框架基于机器学习技术（即遗传编程）开发模型，该模型具有比传统方法更好的泛化能力，并且比隐式建模方法具有更高的透明度。为了进一步提高预测能力，提出了委员会成员选拔的建议。所提出的选择方法改进了当前使用的选择方法，该方法训练了几种模型并且仅选择最佳模型。所提出的选择方法生成混合模型，该模型集成了所生成模型的预测。每个预测都由其他预测同意该预测的可能性加权。所提议的框架是在电吹风机设计上实施的，该电吹风机使用amazon.com的在线评论。