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

ZHANG Yangsen; ZHANG Yaorong; JIANG Yuru; HUANG Gaijuan

首页> 外文期刊>电子学报（英文版） >Multi-feature-Based Subjective-Sentence Classification Method for Chinese Micro-blogs

【24h】

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

机译：基于多特征的中文微博主观句分类方法

获取原文

获取原文并翻译 | 示例

掌桥外文数据库（机构版） >>

开具论文收录证明 >>

文献代查 >>

页面导航

摘要
著录项
相关主题

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.

机译：主观和客观判决的准确分类对于微博语言分析的准备是重要的。由于单个特征类型不能提供足够的主观信息进行分类，因此我们使用多个功能提出支持向量机（SVM）的基于中国微博的分类模型。我们从语音（POS）部分和单词之间的依赖关系中提取了主观特征，并构建了3-POS主体模式图案集和依赖模板集。我们融合了这两种类型的功能，并使用了基于SVM的模型来对中国微博文本进行分类。实验结果表明，使用多个特征时，分类模型的性能显着提高。

著录项

来源
《电子学报（英文版）》 |2017年第6期|1111-1117|共7页
作者
ZHANG Yangsen; ZHANG Yaorong; JIANG Yuru; HUANG Gaijuan;
展开▼
作者单位

Beijing Information and Technology University, Beijing 100192, China;

Beijing Information and Technology University, Beijing 100192, China;

Beijing Information and Technology University, Beijing 100192, China;

Beijing Information and Technology University, Beijing 100192, China;

展开▼
收录信息中国科技论文与引文数据库(CSTPCD);
原文格式 PDF
正文语种 eng
中图分类
关键词

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

摘要

著录项

相关主题

期刊订阅