基于情感角色模型的文本情感分类方法

Classification method of text sentiment based on emotion role model

针对传统情感分类方法因情感项指向不明引发的误判和隐藏观点遗漏等问题,提出一种基于评价对象情感角色模型的文本情感分类方法。该方法首先识别文本中的潜在评价对象,通过局部语义分析对潜在评价对象所在语句进行情感标注,确定潜在评价对象所在语句的正负极性,并定义其情感角色;然后,改进特征权值计算方法,将情感角色对应的倾向值融入模型特征空间中;最后,通过特征聚合对特征空间实现模型降维。实验结果表明,所提方法与提取强主观性情感项作为特征的情感分类方法相比,分类准确率约提高3.2%,可有效改善文本情感分类效果。

In order to solve the problem of misjudgment which due to emotion point to an unknown and missing hidden view in traditional emotion classification method, a text sentiment classification method based on emotional role modeling was proposed. The method firstly identified evaluation objects in the text, and it used the measure based on local semantic analysis to tag the sentence emotion which had potential evaluation object. Then it distinguished the positive and negative polarity of evaluation objects in this paper by defining its emotional role. And it let the tendency value of emotional role integrate into feature space to improve the feature weight computation method. Finally, it proposed the concept named ＂features converge＂ to reduce the dimension of model. The experimental results show that the proposed method can improve the effect and accuracy of3. 2% for text sentiment classification effectively compared with other approaches which tend to pick the strong subjective emotional items as features.