CAJ | 학술논문

中文产品评论特征词与关联的情感词的分类是观点挖掘的重要研究内容之一。该文改进了英文依存关系语法，总结出5种常用的中文产品评论依存关系；利用最大熵模型进行训练，设计了基于依存关系的复合特征模板。实验证明，应用该复合模板进行特征-情感对的提取，系统的查全率和F-score相比于传统方法，分别提高到78.68%和75.36%。
중문산품평론특정사여관련적정감사적분류시관점알굴적중요연구내용지일。해문개진료영문의존관계어법，총결출5충상용적중문산품평론의존관계；이용최대적모형진행훈련，설계료기우의존관계적복합특정모판。실험증명，응용해복합모판진행특정-정감대적제취，계통적사전솔화F-score상비우전통방법，분별제고도78.68%화75.36%。
In recent years, feature-opinion pairs classification of Chinese product review is one of the most important research field in Web data mining technology. In this paper, five types of Chinese dependency relationships for product review have been concluded based on the traditional English dependency grammar. The maximum entropy model is used to predict the opinion-relevant product feature relations. To train the model, a set of feature symbol combinations have been designed by means of Chinese dependency. The experiment result shows that the recall and F-score of our approach could reach 78.68%and 75.36%respectively, which is clearly superior to Hu’s adjacent based method and Popesecu’s pattern based method.