Paper Title
A Target-Dependent Sentiment Analysis Method For Chinese Micro-Blog

Recently with a large number of people publish Micro-blog to share their opinion, Micro-blog becomes an important data resource for opinion mining and sentiment analysis. The traditional methods usually get poor accuracy since they ignore structured semantic information and the target of sentimental expressions. In this paper we propose a novel target-dependent sentiment analysis method. This method obtains the structured information from syntax tree using the convolution tree kernel of Support Vector Machine. The syntax tree is then pruned according to target with the help of domain ontology and syntactic paths library. Using this method we can eliminate the effect of irrelevant appraisal expressions. Experimental results on two corpuses with different targets show that the performance of our method is much better than the traditional methods. Keywords- Pruning Strategy, Sentiment Analysis, Syntax Tree, Tree Kernel