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。
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.
出处
《北京大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2014年第1期67-72,共6页
Acta Scientiarum Naturalium Universitatis Pekinensis
基金
湖南省自然科学基金项目(13JJ4076
11JJ6047)
湖南省教育厅优秀青年项目(13B101)
衡阳市科技计划项目(2012KJ9)资助
关键词
细粒度情绪分析
中文微博
朴素贝叶斯
SVM
KNN
fine-grained sentiment analysis
Chinese microblog
naive Bayesian
support vector machine （SVM）
K Nearest Neighbor （KNN）
