Advertisement

Characterizing Buzz and Sentiment in Internet Sources: Linguistic Summaries and Predictive Behaviors

  • Richard M. Tong
  • Ronald R. Yager
Part of the The Information Retrieval Series book series (INRE, volume 20)

Abstract

Internet sources, such as newsgroups, message boards, and blogs, are an under-exploited resource for developing analyses of community and market responses to everything from consumer products and services, to current events and politics. In this paper, we present an overview of our exploration of effective ways of characterizing this large volume of information. In our approach, we first create time-series that represent the subjects, opinions, and attitudes expressed in the Internet sources, and then generate “Linguistic Summaries” that provide natural and easily understood descriptions of the behaviors exhibited by these time-series.

Keywords

Internet buzz sentiment linguistic summaries marketing research intelligence analysis data mining text mining fuzzy sets time-series analysis 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

7. Bibliography

  1. Dubois, D., Prade, H. and Testemale, C. (1988) Weighted fuzzy pattern matching. Fuzzy Sets and Systems, 28, 313–331.MathSciNetGoogle Scholar
  2. Dubois, D. and Prade, H. (1989) Processing fuzzy temporal knowledge. IEEE Transactions on Systems, Man and Cybernetics, 19, 729–744.CrossRefMathSciNetGoogle Scholar
  3. Dunham, M. (2003) Data Mining. Prentice Hall, Upper Saddle River, NJ.Google Scholar
  4. Hearst, M. (1992) Direction-Based Text Interpretation as an Information Access Refinement. In Jacobs, P. (Ed.) Text Based Intelligent Systems. Lawrence Erlbaum, Mahwah, NJ.Google Scholar
  5. Kacprzyk, J. and Yager, R. (2001) Linguistic summaries of data using fuzzy logic. International Journal of General Systems, 30, 133–154.MathSciNetGoogle Scholar
  6. Kacprzyk, J., Yager, R. and Zadrozny, S. (2001) Fuzzy linguistic summaries of databases for efficient business data analysis and decision support. In Abramowicz, W. and Zaruda, J. (eds.) Knowledge Discovery for Business Information Systems. Kluwer Academic Publishers, Hingham, MA.Google Scholar
  7. Qu, Y., Shanahan, J. and Wiebe, J. (Co-chairs) (2004) Exploring Attitude and Affect in Text: Theories and Applications. AAAI Spring Symposium SS-04-07. AAAI Press, Menlo Park, CA.Google Scholar
  8. Rasmussen, D. and Yager, R. (1997) A fuzzy SQL summary language for data discovery. In Dubois, D., Prade, H. and Yager, R. (Eds.) Fuzzy Information Engineering: A Guided Tour of Applications. 253–264. John Wiley & Sons, New York, NY.Google Scholar
  9. Sack, W. (2000) Conversation Map: A Content-Based Usenet Newsgroup Browser. In Proc. ACM International Conference on Intelligent User Interfaces. New Orleans, LA.Google Scholar
  10. Spertus, E. (1997) Smokey: Automatic Recognition of Hostile Messages. In Proc. 9th Innovative Applications of Artificial Intelligence. Providence, RI.Google Scholar
  11. Subasic, P. and Huettner, A. (2000) Affect Analysis of Text Using Fuzzy Semantic Typing. In Proc. 9th IEEE International Conference on Fuzzy Systems. San Antonio, TX.Google Scholar
  12. Tong, R. (2001) An Operational System for Detecting and Tracking Opinions in On-Line Discussions. In ACM SIGIR 2001 Workshop on Operational Text Classification Systems. New Orleans, LA.Google Scholar
  13. Turney, P. (2002) Thumbs Up or Thumbs down? Semantic Orientation Applied to Unsupervised Classification of Reviews. In Proc. 40th Annual Meeting of the Association for Computational Linguistics. Philadelphia, PA.Google Scholar
  14. Turney, P. and Littman, M. (2003) Measuring Praise and Criticism: Inference of Semantic Orientation from Association. ACM Transactions on Information Systems, 21(4), 315–346.CrossRefGoogle Scholar
  15. Wiebe, J. (2000) Learning Subjective Adjectives from Corpora. In Proc. 17th National Conference on Artificial Intelligence. Austin, TX.Google Scholar
  16. Wiebe, J., Breck, E., Buckley, C., Cardie, C., Davis, P., Fraser, B., Litman, D., Pierce, D., Riloff, E., Wilson, T., Day, D. and Maybury, M. (2003) Recognizing and Organizing Opinions Expressed in the World Press. In New Directions in Question Answering. AAAI Spring Symposium SS-03-07. AAAI Press, Menlo Park, CA.Google Scholar
  17. Wilson, A. and Rayson, P. (1993) The Automatic Content Analysis of Spoken Discourse. In Souter, C. and Atwell, E. (Eds.) Corpus-Based Computational Linguistics. Rodopi, Amsterdam, The Netherlands.Google Scholar
  18. Yager, R. (1991) On linguistic summaries of data. In Piatetsky-Shapiro, G. and Frawley, B. (Eds.) Knowledge Discovery in Databases. 347–363. MIT Press, Cambridge, MA.Google Scholar
  19. Yager, R. (1996) Database discovery using fuzzy sets. International Journal of Intelligent Systems, 11, 691–712.Google Scholar
  20. Yager, R. (1997) Fuzzy temporal methods for video multimedia information systems. Journal of Advanced Computational Intelligence, 1, 37–45.Google Scholar
  21. Zadeh, L. (1975) The concept of a linguistic variable and its application to approximate reasoning: Part 1. Information Sciences, 8, 199–249.zbMATHMathSciNetGoogle Scholar
  22. Zadeh, L. (1999) From computing with numbers to computing with words-From manipulation of measurements to manipulations of perceptions. IEEE Transactions on Circuits and Systems, 45, 105–119.MathSciNetGoogle Scholar

Copyright information

© Springer 2006

Authors and Affiliations

  • Richard M. Tong
    • 1
  • Ronald R. Yager
    • 2
  1. 1.Tarragon Consulting CorporationBerkeleyUSA
  2. 2.Machine Intelligence InstituteIona CollegeNew RochelleUSA

Personalised recommendations