Topic-related Chinese message sentiment analysis

Considering sentiment analysis of microblogs plays an important role in behavior analysis of social media, there has been a significant progress in this area recently. However, most researches are topic-ignored and neglect the sentimental orientation towards different topics. We propose two combined methods for topic-related Chinese message sentiment analysis. One is a graph-based ranking model of LT-IGT which takes both local and global topical information into consideration. And the other is a method of exploring sentimental features on expanded topical words with word embedding which considers both the syntactic and semantic information. These two methods are integrated into a topic-related Chinese message sentiment classifier. Experimental results on SIGHAN8 dataset show the outperformance of this approach compared with other well-known methods on sentiment analysis of topic-related Chinese message.