Three new probabilistic models for dependency parsing: an exploration
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Phrase dependency parsing for opinion mining
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3
MeSoOnTV: a media and social-driven ontology-based TV knowledge management system
Proceedings of the 24th ACM Conference on Hypertext and Social Media
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Opinion mining became an active research topic in recent years due to its wide range of applications. A number of companies offer opinion mining services. One problem that has not been well studied so far is the representation model. In this paper, we propose a novel sentence level sentiment representation model. By taking the observation that lots of sentences which have complicated opinion relations can not be represented well by slots filling or feature-based model, the novel representation model sentiment graph is described in this paper. A supervised structural learning method is presented and used to construct sentiment graphs from sentences. Experimental results in a manually labeled corpus are given to show the effectiveness of the proposed approach.