五邑大学学报：自然科学版
五邑大學學報：自然科學版
오읍대학학보：자연과학판
Journal of Wuyi University(Natural Science Edition)
2012年
4期
66-71
,共6页
倾向性判断%依存关系%词性特征%支持向量机
傾嚮性判斷%依存關繫%詞性特徵%支持嚮量機
경향성판단%의존관계%사성특정%지지향량궤
tendency judgement%dependency%part-of-speech characteristics%support vector machine
将句法平面词的词性特征、依存关系、依存关系中的词性特征、邻接依存关系、邻接依存关系中的词性特征与倾向性词汇和倾向性搭配作为支持向量机（SVM）分类器的特征集，以句子为单位对多个领域的文本进行倾向性判断．通过交叉验证的方式，估计出分类器的精度为95．6％．据此提出句子倾向性分析可不以句子倾向性判断为前提．
將句法平麵詞的詞性特徵、依存關繫、依存關繫中的詞性特徵、鄰接依存關繫、鄰接依存關繫中的詞性特徵與傾嚮性詞彙和傾嚮性搭配作為支持嚮量機（SVM）分類器的特徵集，以句子為單位對多箇領域的文本進行傾嚮性判斷．通過交扠驗證的方式，估計齣分類器的精度為95．6％．據此提齣句子傾嚮性分析可不以句子傾嚮性判斷為前提．
장구법평면사적사성특정、의존관계、의존관계중적사성특정、린접의존관계、린접의존관계중적사성특정여경향성사회화경향성탑배작위지지향량궤（SVM）분류기적특정집，이구자위단위대다개영역적문본진행경향성판단．통과교차험증적방식，고계출분류기적정도위95．6％．거차제출구자경향성분석가불이구자경향성판단위전제．
The objective sentences of multi-domain from views is distinguished by using part of speech, dependency relationship, the part of speech combinations of the two words under one dependency, two adjacent dependencies, the part of speech combinations of the three words under two adjacent dependencies, sentiment words and sentiment collocations as features of SVM classifier. The precision is about 95.6% with 10-fold cross-validation. It is assumed that the sentence tendency judgement is not the premise of the document sentiment analysis.
