Abstract
Online product reviews provide helpful information for user decision-making. However, since user-generated reviews proliferate in recent years, it is critical to deal with the information overload in e-commerce sites. In this paper, we propose an approach to select a small set of representative reviews for each product, which shall consider both the attribute coverage and opinion diversity under the requirement of providing high quality reviews. First, we assign weights to each attribute, which measure the attribute importance and help realize useful review selection; second, we cluster reviews into different groups representing different concerns which lead to better diversification results especially for selecting smaller sets of reviews; finally, we perform a set of experiments on real datasets to verify our ideas.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Carbonell, J., Goldstein, J.: The use of mmr, diversity-based reranking for reordering documents and producing summaries. In: Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 335–336. ACM (1998)
Chang, C.C., Lin, C.J.: Libsvm: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology (TIST)Â 2(3), 27 (2011)
Danescu-Niculescu-Mizil, C., Kossinets, G., Kleinberg, J., Lee, L.: How opinions are received by online communities: a case study on amazon. com helpfulness votes. In: Proceedings of the 18th International Conference on World Wide Web, pp. 141–150. ACM (2009)
De Marneffe, M., MacCartney, B., Manning, C.: Generating typed dependency parses from phrase structure parses. In: Proceedings of LREC, vol. 6, pp. 449–454 (2006)
Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the International Conference on Web Search and Web Data Mining, pp. 231–240. ACM (2008)
Hong, Y., Lu, J., Yao, J., Zhu, Q., Zhou, G.: What reviews are satisfactory: novel features for automatic helpfulness voting. In: Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 495–504. ACM (2012)
Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 168–177. ACM (2004)
Kim, S.M., Pantel, P., Chklovski, T., Pennacchiotti, M.: Automatically assessing review helpfulness. In: Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, pp. 423–430 (2006)
Lappas, T., Crovella, M., Terzi, E.: Selecting a characteristic set of reviews. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 832–840. ACM (2012)
Lappas, T., Gunopulos, D.: Efficient confident search in large review corpora. Machine Learning and Knowledge Discovery in Databases, 195–210 (2010)
Liu, J., Cao, Y., Lin, C., Huang, Y., Zhou, M.: Low-quality product review detection in opinion summarization. In: Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL), pp. 334–342 (2007)
Lu, Y., Zhai, C.X., Sundaresan, N.: Rated aspect summarization of short comments. In: Proceedings of the 18th International Conference on World Wide Web, pp. 131–140. ACM (2009)
Moghaddam, S., Ester, M.: On the design of lda models for aspect-based opinion mining (2012)
Tsaparas, P., Ntoulas, A., Terzi, E.: Selecting a comprehensive set of reviews. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 168–176. ACM (2011)
Tsur, O., Rappoport, A.: Revrank: A fully unsupervised algorithm for selecting the most helpful book reviews. In: International AAAI Conference on Weblogs and Social Media (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yu, W., Zhang, R., He, X., Sha, C. (2013). Selecting a Diversified Set of Reviews. In: Ishikawa, Y., Li, J., Wang, W., Zhang, R., Zhang, W. (eds) Web Technologies and Applications. APWeb 2013. Lecture Notes in Computer Science, vol 7808. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37401-2_70
Download citation
DOI: https://doi.org/10.1007/978-3-642-37401-2_70
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37400-5
Online ISBN: 978-3-642-37401-2
eBook Packages: Computer ScienceComputer Science (R0)