Abstract
In the Web 2.0 era, there has been an explosive growth of user-contributed data on the Web. Among the user-contributed data, the sheer volume of online reviews (or comments) provide enterprise with invaluable market intelligence about potential customers’ preferences for various products and services. However, there has been growing concerns about the quality of these uncontrolled user-contributed online reviews. Despite numerous research work has been conducted on opinion mining and opinion retrieval, little work has been done to develop effective quality metrics to assess the quality of opinionated expressions. To discover rich and accurate business intelligence from online opinionated expressions, an objective quality-based filtering process is essential for any opinion mining systems. The main contribution of this paper is the design, development, and evaluation of a novel multi-facet quality metric for the assessment of the informativeness of opinionated expressions such as online product reviews. Our preliminary experiments show that the proposed multi-facets quality metric is more effective than a quality assessment approach constructed based on user-generated helpful votes.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Oreilly, T.: What is web 2.0: Design patterns and business models for the next generation of software. Communications & Strategies 1 (2007), http://ssrn.com/abstract=1008839
Dellarocas, C.: Strategic manipulation of internet opinion forums: Implications for consumers and firms. Management Science 52, 1577–1593 (2006)
Jindal, N., Liu, B.: Analyzing and detecting review spam. In: Proceedings of the Seventh IEEE International Conference on Data Mining, pp. 547–552 (2007)
Wu, G., Greene, D., Cunningham, P.: Merging multiple criteria to identify suspicious reviews. In: Proceedings of the Fourth ACM Conference on Recommender systems, pp. 241–244 (2010)
Zhang, Z.: Weighing stars: Aggregating online product reviews for intelligent E-commerce applications. IEEE Intelligent Systems 23(5), 42–49 (2008)
Rieh, S.Y.: Judgment of information quality and cognitive authority in the web. JASIST 53(2), 145–161 (2002)
Zhu, X., Gauch, S.: Incorporating quality metrics in centralized/distributed information retrieval on the world wide web. In: Proceedings of the 24th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 288–295 (2000)
MacDonald, C., Ounis, I., Soboroff, I.: Is spam an issue for opinionated blog post search? In: Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 710–711 (July 2009)
Liu, J., Cao, Y., Lin, C.Y., 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 Conference on Computational Natural Language Learning, pp. 334–342. ACL (2007)
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, New York (2009)
Ghose, A., Ipeirotis, P.G.: Designing novel review ranking systems: predicting the usefulness and impact of reviews. In: Gini, M.L., Kauffman, R.J., Sarppo, D., Dellarocas, C., Dignum, F. (eds.) Proceedings of the 9th International Conference on Electronic Commerce, vol. 258, pp. 303–310. ACM, New York (2007)
Kim, S.M., Pantel, P., Chklovski, T., Pennacchiotti, M.: Automatically assessing review helpfulness. In: Jurafsky, D., Gaussier, É. (eds.) Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, pp. 423–430. ACL (2006)
Liu, Y., Huang, X., An, A., Yu, X.: Helpmeter: A nonlinear model for predicting the helpfulness of online reviews. In: Proceedings of the 2008 IEEE International Conference on Web Intelligence, pp. 793–796. IEEE, Los Alamitos (2008)
Liu, Y., Huang, X., An, A., Yu, X.: Modeling and predicting the helpfulness of online reviews. In: Proceedings of the 2008 IEEE International Conference on Data Mining, pp. 443–452. IEEE Computer Society, Los Alamitos (2008)
Jindal, N., Liu, B.: Opinion spam and analysis. In: Proceedings of the 2008 International Conference on Web Search and Web Data Mining, pp. 219–229 (2008)
Salton, G., McGill, M.: Introduction to Modern Information Retrieval. McGraw-Hill, New York (1983)
Porter, M.: An algorithm for suffix stripping. Program 14(3), 130–137 (1980)
Miller, G.A., Beckwith, R., Fellbaum, C., Gross, D., Miller, K.J.: Introduction to wordnet: An on-line lexical database. Journal of Lexicography 3(4), 234–244 (1990)
Riloff, E.M., Wiebe, J.: Learning extraction patterns for subjective expressions. In: Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing, pp. 105–112. Association for Computational Linguistics (2003)
Joachims, T.: Making large–scale svm learning practical. In: Schölkopf, B., Burges, C.J.C., Smola, A.J. (eds.) Advances in Kernel Methods - Support Vector Learning, MIT Press, Cambridge (1999)
Liu, X., Croft, B.: Cluster-based retrieval using language models. In: Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 186–193 (2004)
Ponte, J.M., Croft, W.B.: A language modeling approach to information retrieval. In: Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Melbourne, Australia, pp. 275–281 (1998)
Martinez-Romo, J., Araujo, L.: Web spam identification through language model analysis. In: Proceedings of the Fifth International Workshop on Adversarial Information Retrieval on the Web, pp. 21–28 (2009)
Mishne, G., Carmel, D., Lempel, R.: Blocking blog spam with language model disagreement. In: Proceedings of the First International Workshop on Adversarial Information Retrieval on the Web, pp. 1–6 (2005)
Lau, R., Lai, C., Li, Y.: Leveraging the web context for context-sensitive opinion mining. In: Proceedings of the 2009 IEEE International Conference on Computer Science and Information Technology, pp. 467–471. IEEE, Los Alamitos (2009)
Lai, C., Xu, K., Lau, R., Li, Y., Jing, L.: Toward a language modeling approach for consumer review spam detection. In: Proceedings of the 2010 IEEE International Conference on e-Business Engineering, pp. 1–8. IEEE Computer Society, Los Alamitos (2010)
Kullback, S., Leibler, R.A.: On information and sufficiency. The Annals of Mathematical Statistics 22, 79–86 (1951)
Lafferty, J., Zhai, C.: Document language models, query models, and risk minimization for information retrieval. In: Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 111–119 (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lau, R.Y.K., Zhang, W., Xia, Y., Song, D. (2011). Multi-facets Quality Assessment of Online Opinionated Expressions. In: Chiu, D.K.W., et al. Web Information Systems Engineering – WISE 2010 Workshops. WISE 2010. Lecture Notes in Computer Science, vol 6724. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24396-7_17
Download citation
DOI: https://doi.org/10.1007/978-3-642-24396-7_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24395-0
Online ISBN: 978-3-642-24396-7
eBook Packages: Computer ScienceComputer Science (R0)