Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Expanding domain sentiment lexicon through double propagation
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Extracting and ranking product features in opinion documents
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Latent aspect rating analysis without aspect keyword supervision
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
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This paper proposes a novel two-stage method for opinion words and opinion targets co-extraction. In the first stage, a Sentiment Graph Walking algorithm is proposed, which naturally incorporates syntactic patterns in a graph to extract opinion word/target candidates. In the second stage, we adopt a self-Learning strategy to refine the results from the first stage, especially for filtering out noises with high frequency and capturing long-tail terms. Preliminary experimental evaluation shows that considering pattern confidence in the graph is beneficial and our approach achieves promising improvement over three competitive baselines.