中文摘要：

提出了基于赋权粗糙隶属度的文本情感分类方法.该方法将特征倾向强度引入到文本的向量空间表示法中,建立了基于二元组属性（特征,特征倾向强度）的文本表示模型.提出了基于情感倾向强度序的属性离散化方法,将特征选择寓于离散化过程,达到数据降维的目的.利用特征倾向强度,定义了赋权粗糙隶属度,用于新文本的情感分类.在真实汽车评论语料上,与支持向量机分类模型进行比较实验表明,基于赋权粗糙隶属度的文本情感分类方法在对数据进行一定程度的压缩后仍表现出较好的分类性能.

英文摘要：

Facing with promptly increasing reviews on the Web,it has been great challenge for information science and technology that how people effectively organize and process document data hiding large amounts of information to meet with particular needs.Text sentiment classification aims at developing some new theories and methods to automatically explore the sentiment orientation of a text by mining and analyzing subjective information in texts such as standpoint,view,attitude,mood,and so on.A method of text sentiment classification based on weighted rough membership is proposed in this paper.In the method,the model of text expression is established based on two-tuples attribute（feature,feature orientation intensity）,by introducing feature orientation intensity into the method of vector space representation.An attribute discretization method is proposed based on the sentiment orientation sequence for feature selection unifying the discretization processing to depress data dimension.To utilize the feature orientation intensity,a weighted rough membership is defined for classifying new sentiment text.Compared with SVM classifier,on the reality car review corpus,the proposed method based on rough membership for text sentiment classification has the best performance after data being compressed in a certainty extent for text sentiment classification.