多策略中文微博细粒度情绪分析研究

北京大学学报（自然科学版）

多策略中文微博细粒度情绪分析研究

欧阳纯萍,阳小华,雷龙艳,徐强,余颖,刘志明

南华大学计算机科学与技术学院, 衡阳421001;

收稿日期:2013-07-09 出版日期:2014-01-20 发布日期:2014-01-20

Multi-strategy Approach for Fine-Grained Sentiment Analysis of Chinese Microblog

OUYANG Chunping, YANG Xiaohua, LEI Longyan, XU Qiang, YU Ying, LIU Zhiming

School of Comupter Science and Technology, University of South China, Hengyang 421001;

Received:2013-07-09 Online:2014-01-20 Published:2014-01-20

摘要/Abstract

摘要： 针对中文微博用户的情绪分析问题, 提出一种基于多策略融合的细粒度情绪分析方法。首先采用朴素贝叶斯算法对微博的有无情绪分类问题进行研究, 然后构建有情绪微博的21维特征向量, 最后采用SVM和KNN算法对微博进行细粒度情绪分析。以新浪微博作为实验对象, 结果表明多策略集成方法好于单一分类算法。在多策略集成方法中, “NB+SVM”方法略优于“NB+KNN”方法。

关键词: 细粒度情绪分析, 中文微博, 朴素贝叶斯, SVM, KNN

Abstract: Fine-grained sentiment analysis of Chinese microblog is investigated and a method of multi-strategy fusion is proposed. Firstly, the authors apply naive Bayesian to identify sentiment or non-sentiment about microblog. Secondly, based on emotion ontology, a method for how to form 21 sentiment features vectors of microblog is presented. At last, fine-grained sentiment of microblog is classified based on SVM and KNN respectively. Experiment results show that multi-strategy fusion is better than a single method, in addition, “NB+SVM” strategy is better than “NB+KNN” strategy.

Key words: fine-grained sentiment analysis, Chinese microblog, naive Bayesian, support vector machine (SVM), K Nearest Neighbor (KNN)

中图分类号:

TP391

欧阳纯萍,阳小华,雷龙艳,徐强,余颖,刘志明. 多策略中文微博细粒度情绪分析研究[J]. 北京大学学报（自然科学版）.

OUYANG Chunping,YANG Xiaohua,LEI Longyan,XU Qiang,YU Ying,LIU Zhiming. Multi-strategy Approach for Fine-Grained Sentiment Analysis of Chinese Microblog[J]. Acta Scientiarum Naturalium Universitatis Pekinensis.

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链接本文: https://xbna.pku.edu.cn/CN/

https://xbna.pku.edu.cn/CN/Y2014/V50/I1/67

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