基于用户评论的商品特征提取及特征价格研究

Research on Product Characteristics Extraction and Hedonic Price Based on User Comments

【目的】针对特征价格研究缺乏特征选取标准的现状,基于大规模用户评论,提出一种商品特征的挖掘与选取方法,对特征价格研究进行改进和延伸。【方法】提取用户评论的关键词,通过关键词聚类获取消费者显著偏好的商品特征,在此基础上建立特征价格模型反映特征价格。为验证模型的科学性和有效性,以广州在售新楼盘为例进行实证研究。【结果】基于用户评论挖掘出7个消费者显著偏好的楼盘特征,以此建立的模型拟合优度达0.760, DW统计量为2.013,楼盘有价特征的用户偏好度和价格影响力的相关系数达0.989。【局限】实验数据来源仅局限于房地产网站。【结论】相比已有研究,基于用户评论选取特征构建的模型在拟合优度上有一定提高,能够较准确地评估商品价格,有效避免特征之间的多重共线性问题,还能延伸探究消费者的偏好理性,给企业和消费者行为提供一定的指导依据。

[Objective] This paper proposes a method to extract product characteristics from user comments, aiming to address the issues facing hedonic price research.[Methods] First, we extracted keywords from user comments. Then, we retrieved the product characteristics favored by consumers through keywords clustering, and established the hedonic price model. Finally, we examined the proposed model with the sales of new properties in Guangzhou.[Results] We found seven real estate characteristics of significant consumer preferences from the user comments. The degree of fitting of the model reached 0.760, the DW statistic was 2.013, and the correlation coefficient between user preferences and price of the real estates was 0.989.[Limitations] The experimental data was collected from real estate website only.[Conclusions] The new model based on users comments could accurately evaluate the price of products. It also helps us effectively avoid multiple collinearity problems between independent variables and further explore business and consumer behaviors.