Multi-feature-Based Subjective-Sentence Classification Method for Chinese Micro-blogs

作     者：ZHANG Yangsen ZHANG Yaorong JIANG Yuru HUANG Gaijuan 

作者机构：Beijing Information and Technology University 

出 版 物：《Chinese Journal of Electronics》 (电子学报(英文))

年 卷 期：2017年第26卷第6期

页      面：1111-1117页

核心收录：

学科分类：0808[工学-电气工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 081203[工学-计算机应用技术] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the National Natural Science Foundation of China(No.61370139 No.61602044) 

主　　题：Micro-blog sentiment analysis Subjective and objective classification Support vector machine(SVM) 3-POS subjective pattern set Dependency template set 

摘      要：The accurate classification of subjective and objective sentences is important in the preparation for micro-blog sentiment analysis. Since a single feature type cannot provide enough subjective information for classification, we propose a Support vector machine(SVM)-based classification model for Chinese micro-blogs using multiple features. We extracted the subjective features from the Part of speech(POS) and the dependency relationship between words, and constructed a 3-POS subjective pattern set and a dependency template set. We fused these two types of features and used an SVM-based model to classify Chinese micro-blog text. The experimental results showed that the performance of the classification model improved remarkably when using multiple features.