面向特定领域的产品评价对象自动识别研究

Research on Comment Target Recognition for Specific Domain Products

产品评价对象的自动识别是文本观点信息抽取和倾向性分析中的重要研究课题之一。该文针对汽车评论,提出了一种不依赖外部资源的无指导评价对象自动识别方法。该方法首先综合使用词形模板和词性模板,采用模糊匹配方法和剪枝法抽取候选评价对象。然后,从候选对象集中,采用双向Bootstrapping方法识别出产品评价对象。最后,通过采用K均值聚类方法对产品评价对象进行聚类,实现从评价对象中自动抽取产品名称和产品属性。实验结果表明,该方法对产品评价对象识别的F值达到58.5%,产品名称识别的F值达到69.48%。

The comment target recognition for the products is one of the important topics in text opinion information extraction and the sentiment analysis. For car product reviews, this paper proposes an unsupervised method to recognize comment targets without relying upon additional resources. In this method, we employ the fuzzy match technique for the word templates and part of speech templates and the pruning technique to extract candidate evaluated objects. Then the bidirectional Bootstrapping approach is used to recognize the comment targets from the candidate set. Lastly, the comment targets of the products are clustered by the K means method to recognize the product name and the product attributes. The experimental results indicate that the F-value of the recognition of the comment targets and the product names can achieve 58.5% and 69.48% respectively.