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 low-quality 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.
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Xu, B., Zhao, TJ., Wu, JW., Zhu, CH. (2011). Automatically Ranking Reviews Based on the Ordinal Regression Model. In: Deng, H., Miao, D., Lei, J., Wang, F.L. (eds) Artificial Intelligence and Computational Intelligence. AICI 2011. Lecture Notes in Computer Science(), vol 7004. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23896-3_15
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DOI: https://doi.org/10.1007/978-3-642-23896-3_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23895-6
Online ISBN: 978-3-642-23896-3
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