Customer requirements (CRs) have a significant impact on product design. The existing methods of defining CRs, such as customer surveys and expert evaluations, are time-consuming, inaccurate and subjective. This paper proposes an automatic CRs definition method based on online customer product reviews using the big data analysis. Word vectors are defined using a continuous bag of words (CBOW) model. Online customer reviews are searched by a crawling method and filtered by the parts of speech and frequency of words. Filtered words are then clustered into groups by an affinity propagation (AP) clustering method based on trained word vectors. Exemplars in each clustering group are finally used to define CRs. The proposed method is verified by case studies of defining CRs for product design. Results show that the proposed method has better performance to determine CRs compared to existing CRs definition methods.

客户要求 (CR) 对产品设计有重大影响。现有的定义 CR 的方法，例如客户调查和专家评估，耗时、不准确且主观。本文利用大数据分析，提出了一种基于在线客户产品评论的自动CRs定义方法。词向量是使用连续词袋 (CBOW) 模型定义的。在线客户评论通过爬行方式搜索，并通过词性和词频进行过滤。然后通过基于训练的词向量的亲和传播 (AP) 聚类方法将过滤后的词聚类成组。每个聚类组中的样本最终用于定义 CR。通过为产品设计定义 CR 的案例研究验证了所提出的方法。