Exploring in the weblog space by detecting informative and affective articles

ABSTRACT

Weblogs have become a prevalent source of information for people to express themselves. In general, there are two genres of contents in weblogs. The first kind is about the webloggers' personal feelings, thoughts or emotions. We call this kind of weblogs affective articles. The second kind of weblogs is about technologies and different kinds of informative news. In this paper, we present a machine learning method for classifying informative and affective articles among weblogs. We consider this problem as a binary classification problem. By using machine learning approaches, we achieve about 92% on information retrieval performance measures including precision, recall and F1. We set up three studies on the applications of above classification approach in both research and industrial fields. The above classification approach is used to improve the performance of classification of emotions from weblog articles. We also develop an intent-driven weblog-search engine based on the classification techniques to improve the satisfaction of Web users. Finally, our approach is applied to search for weblogs with a great deal of informative articles.

REFERENCES

• 1: J. Bar-Ilan, An outsider's view on "topic-oriented blogging", Proceedings of the 13th international World Wide Web conference on Alternate track papers & posters, May 19-21, 2004, New York, NY, USA [doi>10.1145/1013367.1013373]
• 2: Rebecca F. Bruce , Janyce M. Wiebe, Recognizing subjectivity: a case study in manual tagging, Natural Language Engineering, v.5 n.2, p.187-205, June 1999 [doi>10.1017/S1351324999002181]
• 3: K. T. Durant and M. D. Smith. Mining Sentiment Classification from Political Web Logs. In Proceedings of Workshop on Web Mining and Web Usage Analysis of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (WebKDD-2006). August, 2006.
• 4: N. Glance, M. Hurst, and T. Tornkiyo. Blogpulse: Automated Trend Discovery for Weblogs. In Proceedings of WWW 2004 Workshop on the Weblogging Ecosystem: Aggregation, Analysis and Dynamics, 2004.
• 5: Daniel Gruhl , R. Guha , David Liben-Nowell , Andrew Tomkins, Information diffusion through blogspace, Proceedings of the 13th international conference on World Wide Web, May 17-20, 2004, New York, NY, USA [doi>10.1145/988672.988739]
• 6: M. Hodder. Live Web Search. http://www2.sims.berkeley.edu/courses/is141/f05/schedule.html
• 7: http://blogsearch.google.com/
• 8: http://spaces.live.com/
• 9: http://www.china.com.cn.
• 10: http://www.sohu.com/
• 11: http://www.technorati.com
• 12: Thorsten Joachims, A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization, Proceedings of the Fourteenth International Conference on Machine Learning, p.143-151, July 08-12, 1997
• 13: Thorsten Joachims, Text Categorization with Suport Vector Machines: Learning with Many Relevant Features, Proceedings of the 10th European Conference on Machine Learning, p.137-142, April 21-23, 1998
• 14: 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]
• 15: Ravi Kumar , Jasmine Novak , Prabhakar Raghavan , Andrew Tomkins, Structure and evolution of blogspace, Communications of the ACM, v.47 n.12, December 2004 [doi>10.1145/1035134.1035162]
• 16: J.D. Lasica, Weblogs: A New Source of Information. In We've got blog: How weblogs are changing our culture, John Rodzvilla (ed). Perseus Publishing, Cambridge, MA, 2002. Also http://www.ojr.org/ojr/lasica/p1019165278.php
• 17: Gilly Leshed , Joseph 'Jofish' Kaye, Understanding how bloggers feel: recognizing affect in blog posts, CHI '06 extended abstracts on Human factors in computing systems, April 22-27, 2006, Montréal, Québec, Canada [doi>10.1145/1125451.1125646]
• 18: A. Mccallum and K. Nigam, A Comparison of Event Models for Naive Byaes Text Classification", In Proceedings of AAAI-98 Workshop on "Learning for Text Categorization", pages 41--48, 1998.
• 19: Qiaozhu Mei , Chao Liu , Hang Su , ChengXiang Zhai, A probabilistic approach to spatiotemporal theme pattern mining on weblogs, Proceedings of the 15th international conference on World Wide Web, May 23-26, 2006, Edinburgh, Scotland [doi>10.1145/1135777.1135857]
• 20: G. Mishne. Experiments with Mood Classification in Blog Posts. In Style 2005- 1st Workshop on Stylistic Analysis of Text for Information Access, at SIGIR 2005, 2005.
• 21: Pew Internet and the American Life Project. http://www.pewinternet.org/PPF/r/186/report_display.asp
• 22: Pew Internet and the American Life Project. 2005. http://www.pewinternet.org/trends/Internet_Activities_12.05.05.htm.
• 23: Pew Internet and the American Life Project. 2006. http://www.pewinternet.org/trends/Internet_Activities_7.19.06.htm
• 24: J. D. M. Rennie, L. Shih, J. Teevan, and D. R. Karger, Tackling the Poor Assumption of Naive Bayes Text Classifiers, In Proceedings of the 20th International Conference on Machine Learning (ICML-2003), Washington DC, USA, 2003.
• 25: C. J. Van Rijsbergen, Information Retrieval, Butterworth-Heinemann, Newton, MA, 1979
• 26: Ellen Riloff , Janyce Wiebe, Learning extraction patterns for subjective expressions, Proceedings of the 2003 conference on Empirical methods in natural language processing, p.105-112, July 11, 2003 [doi>10.3115/1119355.1119369]
• 27: Peter D. Turney, Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews, Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, July 07-12, 2002, Philadelphia, Pennsylvania [doi>10.3115/1073083.1073153]
• 28: V. Vapnik, Principles of Risk Minimization for Learning Theory, In D.S. Lippman, J.E. Moody, and D.S. Touretzky, editors, Advances in Neural Information Processding Systems, Morgan Kaufmann, pages 831--838, 1992.
• 29: Janyce Wiebe, Learning Subjective Adjectives from Corpora, Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence, p.735-740, July 30-August 03, 2000
• 30: Janyce M. Wiebe , Rebecca F. Bruce , Thomas P. O'Hara, Development and use of a gold-standard data set for subjectivity classifications, Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics, p.246-253, June 20-26, 1999, College Park, Maryland [doi>10.3115/1034678.1034721]
• 31: Janyce Wiebe , Theresa Wilson, Learning to disambiguate potentially subjective expressions, proceeding of the 6th conference on Natural language learning, p.1-7, August 31, 2002 [doi>10.3115/1118853.1118887]
• 32: Yiming Yang , Jan O. Pedersen, A Comparative Study on Feature Selection in Text Categorization, Proceedings of the Fourteenth International Conference on Machine Learning, p.412-420, July 08-12, 1997

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.m MISCELLANEOUS

Additional Classification:
  I. Computing Methodologies
  I.5 PATTERN RECOGNITION
      I.5.4 Applications
          Subjects: Text processing

General Terms:
Algorithms, Design, Experimentation, Human Factors, Measurement, Performance

Keywords:
Weblog, affective article, classification, informative article, user intent