Abstract
This study seeks to develop an automatic method to identify product review documents on the Web using the snippets (summary information that includes the URL, title, and summary text) returned by the Web search engine. The aim is to allow the user to extend topical search with genre-based filtering or categorization. Firstly we applied a common machine learning technique, SVM (Support Vector Machine), to investigate which features of the snippets are useful for classification. The best results were obtained using just the title and URL (domain and folder names) of the snippets as phrase terms (n-grams). Then we developed a heuristic approach that utilizes domain knowledge constructed semi-automatically, and found that it performs comparatively well, with only a small drop in accuracy rates. A hybrid approach which combines both the machine learning and heuristic approaches performs slightly better than the machine learning approach alone.
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
Boese, E.S., Howe, A.E.: Effects of Web Document Evolution on Genre Classification. In: Proceedings of the 14th ACM international conference on Information and knowledge management (CIKM 2005), Bremen, Germany, pp. 632–639 (2005)
Chen, H., Dumais, S.T.: Bringing Order to the Web: Automatically Categorizing Search Results. In: Proceedings of the ACM SIGCHI Conference on Human Factors in Computing Systems (CHI 2000), pp. 145–152 (2000)
Choi, B., Yao, Z.: Web Page Classification, Foundations and Advances in Data Mining, Studies in Fuzziness and Soft Computing, vol. 180, pp. 221–274. Springer, Berlin (2005)
Finn, A., Kushmerick, N., Smyth, B.: Genre classification and domain transfer for information filtering. In: Crestani, F., Girolami, M., van Rijsbergen, C.J.K. (eds.) Advances in Information Retrieval. LNCS, vol. 2291, pp. 353–362. Springer, Heidelberg (2002)
Joachims, T.: Text categorization with support vector machines: Learning with many relevant features. In: Proceedings of 10th European Conference on Machine-learning, Chemnitz, Germany, April 21-24, pp. 137–142 (1998)
Jones, K.S., Willet, P.: Readings in Information Retrieval. Morgan Kaufman, San Francisco (1997)
Kessler, B., Nunberg, G., Schutze, H.: Automatic detection of text genre. In: Proceedings of the Eighth Conference on European Chapter of the ACL (Association for Computational Linguistics), pp. 32–38 (1997)
Na, J.-C., Khoo, C., Chan, S., Hamzah, N.B.: A sentiment-based search in digital libraries. In: Proceedings of Joint Conference on Digital Libraries 2005 (JCDL 2005), Denver, pp. 143–144 (2005)
Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up? Sentiment classification using machine-learning techniques. In: Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing, Philadelphia, PA, July 6-7, pp. 79–86 (2002)
Quinlan, R.: C4.5: Programs for Machine Learning. Morgan Kaufman, San Francisco (1993)
Sebastiani, F.: Machine-learning in automated text categorization. ACM Computing Surveys 34(1), 1–47 (2002)
Thet, T.T., Na, J.-C., Khoo, C.S.G.: Filtering Product Reviews from Web Search Results. In: Proceedings of ACM Symposium on Document Engineering (DocEng 2007), Winnipeg, Canada (August 28 - 31, 2007)
Yang, Y., Pedersen, J.O.: A Comparative Study on Feature Selection in Text Categorization. In: Proceedings of the fourteenth International Conference on Machine Learning, pp. 412–420. Morgan Kaufmann, San Francisco (1997)
Zeng, H.-J., He, Q.-C., Chen, Z., Ma, W.-Y., Ma, J.: Learning to Cluster Web Search Results. In: Proceedings of the 27th Annual International ACM SIGIR Conference, Sheffield, UK, pp. 210–217 (2004)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Thet, T.T., Na, JC., Khoo, C.S.G. (2007). Automatic Classification of Web Search Results: Product Review vs. Non-review Documents. In: Goh, D.HL., Cao, T.H., Sølvberg, I.T., Rasmussen, E. (eds) Asian Digital Libraries. Looking Back 10 Years and Forging New Frontiers. ICADL 2007. Lecture Notes in Computer Science, vol 4822. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77094-7_13
Download citation
DOI: https://doi.org/10.1007/978-3-540-77094-7_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-77093-0
Online ISBN: 978-3-540-77094-7
eBook Packages: Computer ScienceComputer Science (R0)