基于单层标注级联模型的篇章情感倾向分析

情感分类是目前篇章情感分析的主要方法,但该方法存在难以融入中文结构特征的问题。针对此问题,采用级联模型对篇章情感倾向进行分析,将篇章情感倾向分析分为两层 小句级和篇章级,对篇章情感倾向分析引入小句级的情感分析。该文使用最大熵模型处理小句级情感分类,小句级的输出作为上层篇章级的输入,并结合句型特征和句子位置等信息作为特征,采用支持向量机模型进行篇章级情感分类。同时对于级联模型中双层标注问题,基于交叉验证的思想提出了单层标注级联模型,避免了多层标注工作以及错误。实验结果表明,该方法的准确率较传统情感分类方法提高了2.53%。

Single-label Cascaded Model for Document Sentiment Analysis

Classification is the main method to analyze the document sentiment polarity,but it is defected in its deficiency in integrating the structure features.A cascaded model for sentiment polarity analysis is proposed to address this issue,which consists of two levels: the clause level and the document level.The document is first segmented into clauses which are classified into positive and negative categories by an Maximum Entropy model.Afterwards,these categories are combined with types and positions of clauses as features for document classification via the Support Vector Machine model.Meanwhile,a Single-label Cascade Model based on cross-validation is proposed.Experimental results prove that the accuracy of the proposed method is improved by 2.53 compared with traditional methods of sentiment classification.