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2011 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies
Opinion Mining with Sentiment Graph
Lyon France
August 22-August 27
ISBN: 978-0-7695-4513-4
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.
Index Terms:
Opinion Mining, Sentiment Graph, Structural learning method
Citation:
Qi Zhang, Yuanbin Wu, Yan Wu, Xuanjing Huang, "Opinion Mining with Sentiment Graph," wi-iat, vol. 1, pp.249-252, 2011 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies, 2011
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