Skip to main content

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 42))

Abstract

This paper presents and explores the idea of deriving numerical indicators from texts, that is, converting text data to numerical data that has predictive or diagnostic value. One application of such a general capability is to the provisional identification of networks, or rather, of associations within networks. Conversely, given a network structure among entities that are associated with various texts, the network structure can itself contribute usefully to construction of indicators derived from texts. The focus of the paper is on basic concepts and methods for deriving indicators from texts. Much research remains to be done.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Archak, N., Ghose, A., Ipeirotis, P.: Show me the money! Deriving the pricing power of product features by mining customer reviews. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2007), San Jose, CA. ACM (August 2007)

    Google Scholar 

  2. Beinhocker, E.D.: The origin of wealth: Evolution, complexity, and the radical remaking of economics. Harvard Business School Press, Boston (2006)

    Google Scholar 

  3. Balakrishnan, K., Ghose, A., Ipeirotis, P.: The impact of information disclosure on stock market returns: The Sarlanes-Oxley Act and the role of media as an information intermediary. In: Workshop on Economics and Information Security (WEIS 2008) (Dartmouth College), File (2008), http://weis2008.econinfosec.org/papers/Ghose.pdf

  4. Blair, D.C., Kimbrough, S.O.: Exemplary documents: a foundation for information retrieval design. Information Processing and Management 38(3), 363–379 (2002)

    Article  Google Scholar 

  5. Cecchini, M.: Quantifying the risk of financial events using kernel methods and information retrieval, Ph.D.thesis, University of Florida, Gainesville, FL (2005)

    Google Scholar 

  6. Chen, G.T., Kimbrough, S., Lee, T.: A note on automated support for product application discovery. In: Dutta, A., Goes, P. (eds.) Proceedings of the Fourteenth Annual Workshop on Information Technologies and Systems (WITS 2004), Washington, D.C, pp. 128–133 (2004)

    Google Scholar 

  7. Dworman, G.O., Kimbrough, S.O., Patch, C.: On pattern-directed search of archives and collections. Journal of the American Society for Information Science 51(1), 14–23 (2000)

    Article  Google Scholar 

  8. Dworman, G.O.: Pattern-oriented access to document collections, Ph.D. thesis, University of Pennsylvania, Philadelphia, PA, Available as a working paper, Department of Operations and Information Management (1999)

    Google Scholar 

  9. Feldman, R., Sanger, J.: The text mining handbook: Advanced approaches in analyzing unstructured data. Cambridge University Press, Cambridge (2007)

    Google Scholar 

  10. Fukuyama, F.: Trust. The Free Press, New York (1995)

    Google Scholar 

  11. Glickman, T.S., Terry, K.S.: Using the news to develop a worldwide database of hazardous events: A report of the results of a 75-day experiment, with recommendations for further action, National Science Foundation research grant no. SBR-9309369 report, Center for Risk Management, Resources for the Future, Washington, DC (1994)

    Google Scholar 

  12. Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proceedings of Knowledge Discovery in Databases, KDD 2004 (2004)

    Google Scholar 

  13. Jackson, P., Moulinier, I.: Natural language processing for online applications: Text retrieval. John Benjamins Publishing Company, Amsterdam (2002)

    Book  Google Scholar 

  14. Kimbrough, S.O., MacMillan, I., Ranieri, J.: Process and system for matching products and markets. United States Patent 7,257,568 (August 14, 2007), www.uspto.gov

  15. Kimbrough, S.O., MacMillan, I., Ranieri, J., Thompson, J.D.: Categorized document bases. United States Patent Application 20070106662 (May 10, 2007), http://www.uspto.gov

  16. Konchady, M.: Text mining application programming. Charles River Media, Boston (2006)

    Google Scholar 

  17. Lee, T.Y.: Use-centric mining of customer reviews. In: Proceedings of the 2004 Workshop on Information Technology and Systems, WITS (2004)

    Google Scholar 

  18. Lee, T.: Learning industry-specific voluntary disclosures from SEC 10-K regulatory filings, Winter Information Systems Conference (University of Utah, UT) (March 2008)

    Google Scholar 

  19. Liu, B., Hu, M., Cheng, J.: Opinion observer: Analyzing and comparing opinions on the web. In: Proceedings of WWW 2005 (2005)

    Google Scholar 

  20. Li, F.: Do stock market investors understand the risk sentiment of corporate annual reports? In: Working paper SSRN 898181, University of Michigan, Ann Arbor, MI (2006)

    Google Scholar 

  21. Lauw, H.W., Lim, E.-P., Pang, H.: TUBE (Text-cUBE) for discovering documentary evidence of associations among entities. In: Proceedings of the 22nd Annual ACM Symposium on Applied Computing, SAC 2007, Seoul, Korea, March 11-15, pp. 824–828. ACM (2007), http://www.acm.org/conferences/sac/sac2007/ ; Indicators from Texts 29 (2009)

  22. Lee, T., Li, S., Wei, R.: Needs-centric searching and ranking based on customer reviews. In: IEEE Conference on Electronic Commerce, Washington, D.C. IEEE (July 2008)

    Google Scholar 

  23. Mieszkowski, K.: Steal this bookmark!, Salon, www.salon.com (February 2005), http://dir.salon.com/story/tech/feature/2005/02/08/tagging/index.html

  24. Moens, M.-F.: Automatic indexing and abstracting of document texts. The Information Retrieval Series, vol. 6. Springer, Germany (2000) ISBN: 978-0-7923-7793-1

    Google Scholar 

  25. Nasukawa, T., Yi, J.: Sentiment analysis: Capturing favorability using natural language processing. In: Proceedings of the Second International Conference on Knowledge Capture (K-CAP 2003) (October 2003)

    Google Scholar 

  26. Popescu, A.-M., Etzioni, O.: Extracting product features and opinions from reviews. In: Proceedings of HLTEMNLP (2005)

    Google Scholar 

  27. Putnam, R.D.: Bowling alone: The collapse and revival of American community. Simon & Schuster, New York (2000)

    Book  Google Scholar 

  28. Scaffidi, C., Bierhoff, K., Chang, E., Felker, M., Ng, H., Chun, J.: Red Opal: Product-feature scoring from reviews. In: ACM Conference on Electronic Commerce, San Diego, CA. ACM (June 2007)

    Google Scholar 

  29. Salton, G., Wong, A., Yang, C.S.: A vector space model for automatic indexing. Communications of the ACM 18(11), 613–620 (1975)

    Article  Google Scholar 

  30. Voss, J.: Tagging, folksonomy & co - renaissance of manual indexing? In: Proceedings of the International Symposium of Information Science, pp. 234–254 (2007)

    Google Scholar 

  31. Yi, J., Nasukawa, T., Bunescu, R., Niblack, W.: Sentiment analyzer: Extracting of sentiments towards a given topic using NLP techniques. In: The Third IEEE International Conference on Data Mining, ICDM 2003 (November 2003)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Kimbrough, S.O., Lee, T.Y., Oktem, U. (2012). On Deriving Indicators from Texts. In: Dolk, D., Granat, J. (eds) Modeling for Decision Support in Network-Based Services. Lecture Notes in Business Information Processing, vol 42. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27612-5_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-27612-5_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-27611-8

  • Online ISBN: 978-3-642-27612-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics