9. Events and Trends in Text Streams

  1. Michael W. Berry2 and
  2. Jacob Kogan3
  1. Dave Engel,
  2. Paul Whitney and
  3. Nick Cramer

Published Online: 4 MAR 2010

DOI: 10.1002/9780470689646.ch9

Text Mining: Applications and Theory

Text Mining: Applications and Theory

How to Cite

Engel, D., Whitney, P. and Cramer, N. (2010) Events and Trends in Text Streams, in Text Mining: Applications and Theory (eds M. W. Berry and J. Kogan), John Wiley & Sons, Ltd, Chichester, UK. doi: 10.1002/9780470689646.ch9

Editor Information

  1. 2

    University of Tennessee, Min H. Kao Department of Electrical, Engineering and Computer Science, Knoxville, TN, USA

  2. 3

    University of Maryland, Baltimore, County Baltimore, MD, USA

Author Information

  1. Pacific Northwest National Laboratory, Richland, WA, USA

Publication History

  1. Published Online: 4 MAR 2010
  2. Published Print: 26 MAR 2010

ISBN Information

Print ISBN: 9780470749821

Online ISBN: 9780470689646

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Keywords:

  • data reduction;
  • event detection;
  • feature extraction;
  • static document collection;
  • text streams;
  • trend detection

Summary

This chapter provides a description of the data and the extraction and reduction of relevant features. It discusses the methodology for the detection of events and trends. The chapter explains the temporally related terms and presents an example to illustrate the capabilities of the technology. It also discusses the differences in the algorithms, contrast the algorithms with other topicality measures, and summarize our technology development. The chapter focuses on processing massive amounts of text streams to identify events that have just occurred or are currently occurring. It compares the results to text analysis results on a static document collection and found that the techniques produce results that are different and enhance those results.

Controlled Vocabulary Terms

data reduction