Abstract
Web users have published huge amounts of opinions about services in blogs, Web forums and other review friendly social websites. Consumers form their judgements to service quality according to a variety of service aspects which may be mentioned in different Web reviews. The research challenge is how to extract service aspects from service related Web reviews for conducting automatic service quality evaluation. To address this problem, this paper proposes four different methods to extract service aspects. Two methods are unsupervised methods and the other two methods are supervised methods. In the first method, we use FP-tree to find frequent aspects. The second method is graph-based method. We employ state-of-the-art machine learning methods such as CRFs (Conditional Random Fields) and MLN (Markov Logic Network) to extract service aspects. Experimental results show graph-based method outperforms FP-tree method. We also find that MLN performs well compared to other three methods.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
References
Parasuraman, A., Zeithaml, V.A., Berry, L.L.: Servqual: A multiple-item scale for measuring consumer perceptions. Journal of Retailing 64 (1988)
Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. In: Proceedings of the 2000 ACM SIGMOD international conference on Management of data, SIGMOD 2000, pp. 1–12. ACM, New York (2000)
Lafferty, J.M.A., Pereira, F.: Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In: Proc. the 18th International Conference on Machine Learning, pp. 282–289. Morgan Kaufmann, San Francisco (2001)
Richardson, M., Domingos, P.: Markov logic networks. Machine Learning 62, 107–133 (2006)
Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proceedings of the tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2004, pp. 168–177 (2004)
Liu, B., Hu, M., Cheng, J.: Opinion observer: analyzing and comparing opinions on the web. In: Proceedings of the 14th international conference on World Wide Web, WWW 2005, pp. 342–351 (2005)
Su, Q., Xu, X., Guo, H., Guo, Z., Wu, X., Zhang, X., Swen, B., Su, Z.: Hidden sentiment association in chinese web opinion mining. In: Proceeding of the 17th international conference on World Wide Web, WWW 2008, pp. 959–968. ACM, New York (2008)
Popescu, A.M., Nguyen, B., Etzioni, O.: Opine: extracting product features and opinions from reviews. In: Proceedings of HLT/EMNLP on Interactive Demonstrations, pp. 32–33. ACL, Morristown (2005)
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Proceedings of the 20th International Conference on Very Large Data Bases, VLDB 1994, pp. 487–499 (1994)
Etzioni, O., Banko, M., Soderland, S., Weld, D.S.: Open information extraction from the web. Commun. ACM 51(12), 68–74 (2008)
Brin, S., Page, L.: The anatomy of a large-scale hypertextual web search engine. Comput. Netw. ISDN Syst. 30(1-7), 107–117 (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hao, J., Li, S., Chen, Z. (2010). Extracting Service Aspects from Web Reviews. In: Wang, F.L., Gong, Z., Luo, X., Lei, J. (eds) Web Information Systems and Mining. WISM 2010. Lecture Notes in Computer Science, vol 6318. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16515-3_40
Download citation
DOI: https://doi.org/10.1007/978-3-642-16515-3_40
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-16514-6
Online ISBN: 978-3-642-16515-3
eBook Packages: Computer ScienceComputer Science (R0)