基于CRFs和领域本体的中文微博评价对象抽取研究

微博情感分析是对微博内容进行细粒度的挖掘,有着重要的研究价值。微博评价对象的抽取是微博情感分析研究的关键问题之一。为了提高中文微博评价对象抽取的准确率,该文在中文微博特征分析和微博评论本体构建研究的基础上,尝试从词、词性、情感词以及本体四个方面进行特征选择,采用CRFs模型对评价对象进行抽取。该文将提出的方法运用到COAE2014测评的Task5评价对象抽取任务中,宏平均准确率达到61.20%,在所有测评队伍中居第一。实验结果表明,将本体特征引入到CRFs模型中,能够有效地提高评价对象抽取的准确率。

Opinion Targets Extraction from Chinese Microblogs Based on Conditional Random Fields and Domain Ontology

Fine-grained sentiment analysis of Microblogs is very important. The extraction of opinion targets from opinion sentence is the key issue to sentiment analysis of Microblogs. To improve the performance of opinion targets extraction, this paper proposes to select features from words, parts of speech, emotional words and ontology, based on the characteristics of Chinese microblog and the construction of microblogging comment ontology, and then uses CRFs model to evaluate object extraction. At last, we apply the proposed method to Task5 of COAE2014. The accuracy of the evaluation object extraction is 61.20 percent, ranking first in all the evaluation team. The experiment results show that it is possible to effectively improve the accuracy of the evaluation opinion targets extraction to introduce the ontology into CRFs Model.