基于产品评论情感分析的用户满意度挖掘

Mining User Satisfaction Based on Product Review Sentiment Analysis

互联网的广泛应用和电子商务的迅速发展使得用户满意度分析的重要性日益凸显.传统的用户满意度研究都是以认知理论为基础的小样本分析,难以适应海量网络购物数据的应用场景.针对此,提出了一种基于产品评论情感分析的用户满意度挖掘方法,首先选择用户评论为研究对象,然后运用半监督的递归自编码深度学习模型进行产品评论级情感倾向性分析,最后对用户满意度的动态演化模式进行灰色建模与预测.3种京东商品的评论数据的实验表明方法的有效性.

The extensive usage of Internet and the rapid development of e-commerce provide unprecedented opportunities as well as challenges in developing the customer satisfaction system.However,traditional user satisfaction management tools are built upon cognitive-theory-based small sample analysis. These methods are difficult to be adapted to exploit massive customer review data.An approach to mining customer＇s satisfaction based on product reviews is proposed. First,it select review data as research objects, and then a semi-supervised recursive auto-encoder based deep learning model is developed to discover the review level sentiment orientation. Finally,a grey prediction model is applied to discover the dynamic evolution of customers＇ satisfaction. Experimental results on three JD product review datasets show the validity of the proposed method.