期刊论文

Ouyang, C.

中文

北京大学学报(自然科学版)

0479-8023

2014

50

1

67-72

10.13209/j.0479-8023.2014.028

13JJ4076:湖南省自然科学基金 11JJ6047:湖南省自然科学基金 13B101:湖南省教育厅优秀青年项目 2012KJ9:衡阳市科技计划

本校为第一且通讯机构

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。

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"...