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In the most existed text-mining schemes for customer reviews, explicit features are usually concerned while implicit features are ignored, which probably leads to incomplete or incorrect results. In fact, it is necessary to consider implicit features in customer review mining. Focusing on the identification of implicit feature, a novel scheme based on hybrid rules is proposed, which mixed statistical rule, dependency parsing and conditional probability. Explicit product features are firstly extracted according to FP-tree method and clustered. Then, association pairs are obtained based on dependency parsing method and the production of frequency and PMI. Finally, implicit features are identified by considering the association pairs and conditional probability of verbs, nouns and emotional words. The proposed scheme is tested on a public cellphone reviews corpus. The results show that our scheme can effectively find implicit features in customer reviews. Therefore, our research can obtain more accurate and comprehensive results from the customer reviews.
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