Abstract
Opinion mining systems suffer a great loss when unknown opinion targets constantly appear in newly composed reviews. Previous opinion target extraction methods typically consider human-compiled opinion targets as seeds and adopt syntactic/statistic patterns to extract opinion targets. Three problems are worth noting. First, the manually defined opinion targets are too large to be good seeds. Second, the list that maintains seeds is not powerful to represent relationship between the seeds. Third, one cycle of opinion target extraction is barely able to give satisfactory performance. As a result, coverage of the existing methods is rather low. In this paper, the opinion target network (OTN) is proposed to organize atom opinion targets of component and attribute in a two-layer graph. Based on OTN, a bootstrapping method is designed for opinion target extraction via generalization and propagation in multiple cycles. Experiments on Chinese opinion target extraction show that the proposed method is effective.
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Xia, Y., Hao, B., Wong, KF. (2009). Opinion Target Network and Bootstrapping Method for Chinese Opinion Target Extraction. In: Lee, G.G., et al. Information Retrieval Technology. AIRS 2009. Lecture Notes in Computer Science, vol 5839. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04769-5_30
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DOI: https://doi.org/10.1007/978-3-642-04769-5_30
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