Sentiment Target Extraction Based On Crfs With Multi-Features For Chinese Microblog

Sentiment target extraction on Chinese microblog has attracted increasing research attention. Most previous work relies on syntax, such as automatic parse trees, which are subject to noise for informal text such as microblog. In this paper, we propose a modified CRFs model for Chinese microblog sentiment target extraction. This model see the sentiment target extraction as a sequence-labeling problem, incorporating the contextual information, syntactic rules and opinion lexicon into the model with multi-features. The major contribution of this method is that it can be applied to the texts in which the targets are not mentioned in the sequence. Experimental results on benchmark datasets show that our method can consistently outperform the state-of-the-art methods.