基于层次结构的多策略中文微博情感分析和特征抽取

随着Web2.0时代的兴起,与微博相关的研究得到了学术界和工业界的广泛关注。该文使用新浪API获取数据,针对中文微博消息展开了情感分析方面的研究。我们对于三种情感分析的方法进行了深入研究,包括表情符号的规则方法、情感词典的规则方法、基于SVM的层次结构的多策略方法,实验表明基于SVM的层次结构多策略方法效果最好。其次,针对层次结构的多策略方法的特征选择进行了详细分析,包括主题无关、主题相关的特征。实验表明使用主题无关的特征时获得的准确率为66.467%。引入主题相关的特征后,准确率提升至67.283%。

Hierarchical Structure Based Hybrid Approach to Sentiment Analysis of Chinese Micro Blog and Its Feature Extraction

With the development of Web 2.0,micro blog has drawn substantial attention from both academia and industry communities.This paper utilizes micro blog API from Sina and carries out sentiment analysis on Chinese micro blog.We compare performances of three method,based on the emoticon,the sentiment lexicon and the hybrid approach over hierarchical structure using SVM,respectively.Through the experiments,we find that SVM based hybrid approach achieves the best performance.Furthermore,we analyze the contribution of various features in this model,including target-independent features and target-dependent features.Experimental results show that SVM based method can gain an accuracy of 66.467% with target-independent features,and an improved accuracy of 67.283% with the addition of target-dependent features.