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房磊 刘彪 黄民烈
Journal of computer science and technology, 07/2015, Letnik: 30, Številka: 4Journal Article
Product feature and opinion word extraction is very important for fine granular sentiment analysis. In this paper, we leverage large-scale unlabeled data for joint extraction of feature and opinion words under a knowledge poor setting, in which only a few feature-opinion pairs are utilized as weak supervision. Our major contributions are two- fold: first, we propose a data-driven approach to represent product features and opinion words as a list of corpus-level syntactic relations, which captures rich language structures; second, we build a simple yet robust unsupervised model with prior knowledge incorporated to extract new feature and opinion words, which obtains high performance robustly. The extraction process is based upon a bootstrapping framework which, to some extent, reduces error propagation under large data. Experimental results under various settings compared with state-of-the-art baselines demonstrate that our method is effective and promising.
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Leto | Faktor vpliva | Izdaja | Kategorija | Razvrstitev | ||||
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JCR | SNIP | JCR | SNIP | JCR | SNIP | JCR | SNIP |
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in: SICRIS
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