面向产品属性的用户情感模型

传统情感模型在分析商品评论中的用户情感时面临两个主要问题:1)缺乏针对产品属性的细粒度情感分析;2)自动提取的产品属性其数量须提前确定。针对上述问题,提出了一种细粒度的面向产品属性的用户情感模型(USM)。首先,利用分层狄利克雷过程(HDP)将名词实体聚类形成产品属性并自动获取其数量;然后,结合产品属性中名词实体的权重和评价短语以及情感词典作为先验,利用潜在狄利克雷分布(LDA)对产品属性进行情感分类。实验结果表明,该模型具有较高的情感分类准确率,情感分类平均准确率达87%。该模型与传统的情感模型相比在抽取产品属性和评价短语的情感分类上具有较高的准确率。

User sentiment model oriented to product attribute

The traditional sentiment model faces two main problems in analyzing user's emotion of product reviews: 1) the lack of fine-grained emotion analysis for product attributes; 2) the number of product attributes shall be defined in advance. In order to alleviate the problems mentioned above, a fine-grained model for product attributes named User Sentiment Model (USM) was proposed. Firstly, the entities were clustered in product attributes by Hierarchical Dirichlet Processes (HDP) and the number of product attributes could be obtained automatically. Then, the combination of the entity weight in product attributes, the evaluation phrase of product attributes and sentiment lexicon was considered as prior. Finally, Latent Dirichlet Allocation (LDA) was used to classify the emotion of product attributes. The experimental results show that the model achieves a high accuracy in sentiment classification and the average accuracy rate of sentiment classification is 87%. Compared with the traditional sentiment model, the proposed model obtains higher accuracy on extracting product attributes as well as sentiment classification of evaluation phrases.