融合特征情感和产品参数的客户感知偏好模型

在线评论作为一种公开、可获取的信息资源，蕴含了关于产品质量的各种有价值的信息，对这些信息的分析与挖掘有助于企业了解客户的需求和偏好。目前，基于在线评论的客户偏好模型考虑了特征观点和评论数量，忽视了产品参数对消费者购买决策的影响。因此，本文提出了一种融合特征情感和产品参数的客户感知偏好模型。首先，利用在线评论提取客户讨论的产品特征，并识别产品特征的情感极性，从而进一步计算特征正负面情感得分，生成产品的特征情感。然后，结合企业定义的产品参数，构建特征情感和产品参数融合作用对产品销量影响的计量经济模型，分析客户对产品的感知偏好。最后，为验证模型的有效性，获取了汽车之家网站的39款汽车产品（SUV车型）的评论文本，并持续8个月的销量跟踪。研究结果显示本文提出的模型比信息增益和TF-IDF方法能够更加准确地获取客户的感知偏好。此外，研究结果解释了特征情感和产品参数与产品销量的关联关系，为企业的市场营销和产品设计决策提供了理论基础。

Customer Preference Model Considering Feature Sentiment and Product Parameters

Online review, which is access to the public, contains a variety of valuable information about product quality. Analyzing and mining of these information is very helpful for the enterprises to understand customer needs and preferences. Currently, the customer preference model based on online reviews considers the number and sentiment of the reviews, but ignores the impact of product parameters on consumers' purchasing decisions. Therefore, a customer preferences model that fuses feature sentiment and product parameters is proposed. First, the product features discussed in the online reviews must be extracted, and the sentiment polarities of the product features should be identified, thereby generating the feature sentiment by measuring the score of positive and negative sentiment. Then, an econometric model combining product parameters provided by the enterprises is built to reflect the influence of the feature sentiment and product parameters on product sales. Finally, in order to verify the effectiveness of our model, an online review text of 39 automotive products (SUV models) from autohome.com has been obtained, and sales tracking has been kept for 8 months. The results show that our model has better performance than the methods of information gain and TF-IDF. It is found that most product features and some product parameters have significant impacts on product sales. Besides, product parameters can enhance customers' perceptions on product quality. In addition, our conclusions explain the impacts of feature sentiment and product parameters on product sales, which provide a theoretical basis for marketing strategy and making product design decisions.