关键词: 依存句法解析, 文本情感分类, 向量空间模型, 深度信念网络

Abstract: In order to improve the semantic understanding of terms in vector space model, this paper proposes a new text representation method based on dependency parser. Based on the dependency parser tree transformed from the text, some useless nodes are merged and deleted according to Chinese grammar rules firstly. Then the retained tree will be transformed into triple dependency relation features. The generalized triple dependency relation features will be used as the feature set of vector space model. For verifying the new method’s efficiency, different feature sets based on bag-of-words are extracted to be the baseline. All of the feature vectors are applied into SVM and DBN to get the classification accuracy. The results indicate that triple dependency relation features is rich of semantic information.

Key words: dependency syntactic relation, sentiment classification, vector space model, deep belief network