A Topic Detection and Tracking System with TF-Density

  • Shu-Wei Liu
  • Hsien-Tsung Chang
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 156)


In the past, news consumption took place predominantly via newspapers and were hard to track. Nowadays, the rapid growth of the Internet means that news are continually being shared and stored on a previously unimaginable scale. It is now possible to access several news stories on the same topic on a single web page. In this paper, we proposed a topic detection and tracking system with a new word measurement scheme named TF-Density. TF-Density is a new algorithm modified from the well-known TF-IWF and TF-IDF algorithms to provide a more precise and efficient method to recognize the important words in the text. Through our experiments, we demonstrated that our proposed topic detection and tracking system is capable of providing more precise and convenient result for the tracking of news by users.


Word Frequency News Story Topic Cluster News Source Term Vector 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Department of CSIEChang Gung UniversityTaoyuanTaiwan

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