Enhancing clustering blog documents by utilizing author/reader comments

ABSTRACT

Blogs are a new form of internet phenomenon and a vast everincreasing information resource. Mining blog files for information is a very new research direction in data mining. Blog files are different from standard web files and may need specialized mining strategies. We propose to include the title, body, and comments of the blog pages in clustering datasets from blog documents. In particular, we argue that the author/reader comments of the blog pages may have more discriminating effect in clustering blog documents. We constructed a word-page matrix by downloading blog pages from a well-known website and experimented a k-means clustering algorithm with different weights assigned to the title, body, and comment parts. Our experimental results show that assigning a larger weight value to the blog comments helps the k-means algorithm produce better clustering solutions. The experimental results confirm our hypothesis that the author/reader comments of the blog files are very useful in discriminating blog files.

REFERENCES

• 1: Aschenbrenner, A., and Miksch, S. Blog mining in a corporate environment, Technical Report ASGAARD-TR-2005-11, Smart Agent Technologies, 2005.
• 2: Michael W. Berry , Murray Browne, Email Surveillance Using Non-negative Matrix Factorization, Computational & Mathematical Organization Theory, v.11 n.3, p.249-264, October 2005 [doi>10.1007/s10588-005-5380-5]
• 3: Rebecca Blood, The Weblog Handbook: Practical Advice on Creating and Maintaining Your Blog, Perseus Publishing, 2002
• 4: Deerwester, S., Dumais, S. T., Furnas, G. W., Landauer, T. K., and Harshman, R. Indexing by latent semantic analysis, Journal of the Society of Information Science, 41(1990), 391--407.
• 5: Inderjit S. Dhillon , Dharmendra S. Modha, Concept decompositions for large sparse text data using clustering, Machine Learning, v.42 n.1/2, p.143-175, Jan. 2001 [doi>10.1023/A:1007612920971]
• 6: Susan C. Herring , Lois Ann Scheidt , Sabrina Bonus , Elijah Wright, Bridging the Gap: A Genre Analysis of Weblogs, Proceedings of the Proceedings of the 37th Annual Hawaii International Conference on System Sciences (HICSS'04) - Track 4, p.40101.2, January 05-08, 2004
• 7: Hoyt C. Mining the blogosphere, the HUB Magazine, January 10, 2006, http://hubmagazine.com/?p=76, last accessed on October 30, 2006.
• 8: Ravi Kumar , Jasmine Novak , Prabhakar Raghavan , Andrew Tomkins, On the bursty evolution of blogspace, Proceedings of the 12th international conference on World Wide Web, May 20-24, 2003, Budapest, Hungary [doi>10.1145/775152.775233]
• 9: Liu, H., Li, J., and Wong, L. A comparative study on feature selection and classification methods using gene expression profiles and proteomic patterns. Genome Informatics, 13(2002), 51--60.
• 10: MacQueen, J. B. Some methods for classification and analysis of multivariate observations. In Proceedings of the 5^th Symposium on Mathematics, Statistics, and Probability, University of California Press, 1967, 281--297.
• 11: Malkin, M. All about the Minnesota school shooter, March 23, 2005, http://michellemalkin.com/archives/001837.htm, last accessed on November 1, 2006.
• 12: Nicolov, N., Salvetti, F., Liberman, M., and Martin, J. H. Computational approaches to analyzing weblogs. In Papers from 2006 AAAI Spring Symposium, 2006.
• 13: Gerard Salton , Michael J. McGill, Introduction to Modern Information Retrieval, McGraw-Hill, Inc., New York, NY, 1986
• 14: Sifry, D. Sifry's alerts, at http://www.sifry.com/alerts/archives/000436.html, accessed on October 31, 2006.
• 15: Tang, B., Shepherd, M., Milios, E., and Heywood, M. Comparing and combing dimension reduction techniques for efficient test clustering, In Proceedings of the Workshop on Feature Selection for Data Mining, SIAM Data Mining, 2005.
• 16: Torio, J. Blogs, A Global Conversation, Master's Thesis, Syracuse University, 2005.
• 17: Shuting Xu , Jun Zhang, A parallel hybrid web document clustering algorithm and its performance study, The Journal of Supercomputing, v.30 n.2, p.117-131, November 2004 [doi>10.1023/B:SUPE.0000040611.25862.d9]
• 18: Oren Zamir , Oren Etzioni, Web document clustering: a feasibility demonstration, Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval, p.46-54, August 24-28, 1998, Melbourne, Australia [doi>10.1145/290941.290956]
• 19: Zhao, Y., and Karypis, G. Criterion Function for Document Clustering Experiments and Analysis, Technical Report #01--40, Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN, 2001.

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval

General Terms:
Performance

Keywords:
blog, blogosphere, clustering, comment, data mining