基于句法分析和属性概率权重的跨语言情感分类算法

摘要

在篇章级情感分类研究方法中,算法常仅考虑情感的分布信息,忽略情感知识的语义信息,影响跨语言情感分类的准确率.针对上述问题,文中提出基于句法分析和属性概率权重的跨语言情感分类算法.首先,通过句法分析得到表征词语之间关系的依赖对,再将依赖对翻译到目标语言.然后,基于词典极性的分布信息与文档情感分类的相关性,将类别属性的语义权重特征融合到朴素贝叶斯分类方法中,实现新的分类方法.使用带标签的英文分类数据集作为训练语料,标准中文数据集作为测试语料进行实验,结果表明文中算法的有效性.%In the document-level sentiment classification methods,only the distribution information of emotion is taken into account, while the semantic emotion knowledge is ignored. To solve these problems, a cross-language sentiment classification algorithm based on the dependency analysis and property probability weight is proposed. Firstly, dependency relations are got by dependency relation parsing before translation. Then, based on the correlation between the distribution of dictionary polar and the document-level sentiment classification, the weight feature of property probability is merged into Naive bayesian classification to improve the classification effect. Finally, extensive experiments are performed on English datasets for training and standard Chinese datasets for testing. The results show that the proposed algorithm is superior to other existing algorithms in performance.