CASIA OpenIR  > 模式识别国家重点实验室  > 自然语言处理
Sentence Sentiment Analysis Based on Cascaded CRFs Model
Liu Kang (刘康); Zhao Jun (赵军)
Source PublicationChinese Journal of Computers
AbstractThis paper focuses on the task of sentence sentiment analysis. The traditional sentence sentiment analysis methods have the following two problems. First, the classification method cannot consider the contextual information; Second, the label redundancy in the single-layer model has negative effect on the labeling accuracy of the second layer. Aiming at these two problems, this paper proposed a new sentence sentiment analysis method based on cascaded CRFs model, which used multiple CRFs models to computesentence sentiment and sentiment strength in a cascaded way. The cascaded frame can alleviate the negative impact of related labels on the labeling accuracy, and on the other hand CRFs model can consider the contextual information. This method can improve the accuracy of sentence sentiment strength label while labeling sentence sentiment effectively. The experiments can validate this method. The performance of experiments can be improved greatly than SVM method and classical CRFs model.
KeywordSentence Sentiment Analysis
Document Type期刊论文
Recommended Citation
GB/T 7714
Liu Kang ,Zhao Jun . Sentence Sentiment Analysis Based on Cascaded CRFs Model[J]. Chinese Journal of Computers,2008(22):123-128.
APA Liu Kang ,&Zhao Jun .(2008).Sentence Sentiment Analysis Based on Cascaded CRFs Model.Chinese Journal of Computers(22),123-128.
MLA Liu Kang ,et al."Sentence Sentiment Analysis Based on Cascaded CRFs Model".Chinese Journal of Computers .22(2008):123-128.
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