基于层叠CRFs模型的句子褒贬度分析研究

Sentence Sentiment Analysis Based on Cascaded CRFs Model

本文研究句子的褒贬度分析问题。针对传统的基于分类的句子褒贬度分析方法不能考虑上下文信息的问题,以及基于单层模型的句子褒贬度分类方法中的由于标记冗余引起的分类精度不高问题,本文提出了基于层叠式CRFs模型的句子褒贬度分析方法。该方法利用多个CRFs模型从粗到细分步地判断句子的褒贬类别及其褒贬强度,其中层叠式框架可以考虑句子褒贬类别与褒贬强度类别之间的层级冗余关系,而CRFs模型可以利用上下文信息对于句子褒贬类别和强度的影响。该方法在有效识别句子褒贬度的同时,提高了句子褒贬强度判别的准确度。实验证明相对于传统分类方法和单层CRFs模型,本文的方法取得了良好的效果。

This 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 compute sentence 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.