大多数研究者对微博倾向性分析过多关注的是情感词、形容词和否定词，忽略了关联词对其情感倾向的影响。为了提高微博情感倾向性分析的准确率，提出了融合关联词的微博倾向性分析方法，考虑微博文本中形容词、程度副词以及关联词之间的组合关系。本文充分考虑了关联词的结构特点并在已有词典的基础上构建专门用于微博倾向性分析的微博词典、否定词词典和关联词词典，同时考虑到网络新词对微博倾向性的影响，还构建了一个全新的网络新词词典。借助支持向量机（Support vector ma‐chine ，SVM ）将微博文本分为负向、正向和中性3类，通过结合情感词典和SVM的方法提高微博文本倾向性分析的准确率。通过对COASE 2014数据实验可以表明，本文方法对微博倾向性分析取得了较好的效果。%At present ,a larger number of researchers focus on Micro‐blog orientation on the emotional words ,adverb and negative words without considering the impact of connectives .To improve the accura‐cy of orientation analysis ,a method of analyzing Mico‐blog orientation is proposed .In the paper ,we suf‐ficiently analyze the structure characteristics of associated words and consider the combination laws of negative words ,adversative words and conjunctions in Microblog .In addition ,a specific dictionary is created based on the existing resources ,which contains a turning words lexicon ,a connective lexicon and a negative words lexicon .At the same time ,we take into account the impact of new network words and phrases of the microblog text ,so we also build a new network words dictionary .Therefore ,the Microb‐log texts are classified into three categories including negative ,positive and neutral one by support vector machine (SVM ) .By combining Lexicon‐based and SVM machine learning method ,better accuracy of classification can be achieved .Experimental results verify that the method achieves higher classification accuracy through experiments using COASE 2014 .
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