中文摘要：

基于词袋模型的文本情感倾向性分析没有考虑句子的句法结构对句子语义的理解，基于依存句法分析的方法试图解决这一问题．目前基于依存句法分析的方法对影响文本情感的依存关系的选择多根据人为观察，带有随意性．根据影响句子情感倾向性的原极性、修饰极性和动态极性，1）找出了影响句子情感倾向性的4种词性：形容词、动词、副词和名词；2）从词性和汉语句子成分理解的角度，逐一分析了24种依存关系对甸子情感计算的影响，找出了可能影响句子情感倾向性的8种依存关系；3）根据这8种依存关系中可能的词性组合设计了6种情感计算规则，并提出了基于二叉树的情感计算策略，设计了情感计算二叉树的构建算法和基于情感计算二叉树的情感计算算法；4）在Web金融信息上进行了实验测试，实验结果表明了该方法的有效性．

英文摘要：

Sentiment orientation analysis has attracted a great deal of attention recently due to many practical applications and challenging research problems. Traditional text sentiment analysis method based on bag of words model does not take into account the syntactic structure of the sentence, while the method of text sentiment analysis based on dependency parsing model tries to solve this problem. At present, the existed methods based on dependency parsing mainly focus on the author＇s observation and lead to be subjectivity and arbitrariness when selecting dependency pair. Therefore, this paper firstly finds out 4 kinds of parts of speech which could affect sentence sentiment, such as adjectives, verbs, adverbs and nouns, from the original polarity, modified polarity and dynamic polarity of emotion words. Secondly, we analyze the effects of 24 kinds of dependency pair on the sentence sentiment computation and select 8 kinds of dependency pair according to the part of speech and Chinese modification understanding. Thirdly, 6 kinds of sentiment computing rules are designed from the combination of the part of speech of the above 8 kinds of dependency pair and then the sentiment computation method based on two binary tree is proposed, which includs the construction method and the sentiment computation method. Finally, the experiments are carried out on the Web financial information and the results prove the effectiveness of this method.