一种基于情感依存元组的简单句情感判别方法

A Simple-sentence Sentiment Classification Method Based on Emotional Dependency Tuples

基于依存句法"动词配价"原理与组块的概念,提出以情感依存元组(EDT)作为中文情感表达的基本单位。它以句中能承载情感的几类实词作为中心词,修饰词依附于中心词,程度词和否定词依附于中心词和修饰词。该文对句子进行句法分析,在句法树和依赖关系中按规则提取情感依存元组,建立简单句情感依存元组判别模型计算情感倾向性。针对COAE2014评测公布的网络新闻语料,将该方法分别与有监督分类算法(KNN、SVM)和半监督算法(K-means)进行实验对比。结果表明,基于EDT的情感分类性能与有监督的机器学习算法相当,远高于半监督的聚类算法。

Based on the principle of ＂Verb Valency＂ and the dependency parsing, this paper proposes to treat the e motional dependency tuple （EDT） as the basic unit of Chinese emotional expression. An EDT consists of the core words （i. e. several selected categories of contents words expressing emotion in the sentence）, the modifier attached to the core words, and the degree or negative words attached to either the core words or the modifiers. The EDTs are extracted from the parsed sentences, and the emotional dependency tuples based sentiment classification model is established. Experimented on the web news corpus released by COAE2014, the proposed method outperforms the semisupervised algorithm（K-MEANS）, producing comparable results to the supervised classification algorithms （KNN,SVM）.