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• W79612384 abstract "With the rapid development of Internet and E-commerce, the quantity of product reviews on the web grows very fast, but the review quality is inconsistent. This paper addresses the problem of automatically ranking reviews. A specification for judging the reviews quality is first defined and thus ranking review is formalized as ordinal regression problem. In this paper, we employ Ranking SVM as the ordinal regression model. To improve system performance, we capture many important features, including structural features, syntactic features and semantic features. Experimental results indicate that Ranking SVM can obviously outperform baseline methods. For the identification of lowquality reviews, the Ranking SVM model is more effective than SVM regression model. Experimental results also show that the unigrams, adjectives and product features are more effective features for modeling." @default.
• W79612384 created "2016-06-24" @default.
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• W79612384 date "2011-01-01" @default.
• W79612384 modified "2023-10-08" @default.
• W79612384 title "Automatically Ranking Reviews Based on the Ordinal Regression Model" @default.
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• W79612384 doi "https://doi.org/10.1007/978-3-642-23896-3_15" @default.
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