Abstract: Sentiment polarity analysis of Web news comments will be of great importance in analyzing public opinion and mastering the polls in the Internet age. Nowadays researches are focused on analyzing the comment itself and ignoring the structural?relationships between the comments, so the paper uses the relationships to build comments relationship tree, and constructs the sentiment polarity decision?rules with the tree. After pre-processing, it takes the both methods based on sentiment dictionary and  on SVM to analyze the sentiment polarity, extends the sentiment dictionary dynamically and designs the classifier. Experiments demonstrate that the methods get higher precision after the comments relationship tree is bringing in.

Key words: sentiment polarity, Web news comments;comments structure, extension emotion dictionary, Support Vector , Machines（SVM）

摘要： 网络新闻评论情感分析对于互联网时代分析舆情、掌握民调具有重要意义。目前研究聚焦在评论自身的分析而忽略评论间的结构关系，因此利用该关系生成评论关系树，并基于评论关系树建立情感极性判别规则。将评论经过预处理后，同时采用基于扩展情感词典和支持向量机两种方法来进行情感极性分析，动态扩展了情感词典，设计了情感极性分类器。实验结果表明，在利用了评论结构关系之后，两种方法的分析准确率均较没利用该关系之前有了明显的提升。

关键词: 情感极性, 网络新闻评论, 评论关系, 扩展情感词典, 支持向量机