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 accuracy of orientation analysis, a method of analyzing Mico-blog orientation is proposed. In the paper, we sufficiently 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 Microblog 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.