Advances in Data Analysis and Classification

, Volume 11, Issue 1, pp 159–178 | Cite as

Evaluation of the evolution of relationships between topics over time

  • Wolfgang GaulEmail author
  • Dominique Vincent
Regular Article


Topics that attract public attention can originate from current events or developments, might be influenced by situations in the past, and often continue to be of interest in the future. When respective information is made available textually, one possibility of detecting such topics of public importance consists in scrutinizing, e.g., appropriate press articles using—given the continual growth of information—text processing techniques enriched by computer routines which examine present-day textual material, check historical publications, find newly emerging topics, and are able to track topic trends over time. Information clustering based on content-(dis)similarity of the underlying textual material and graph-theoretical considerations to deal with the network of relationships between content-similar topics are described and combined in a new approach. Explanatory examples of topic detection and tracking in online news articles illustrate the usefulness of the approach in different situations.


Topic relationships Topic trend detection Text processing Content-(dis)similarity Information clustering 

Mathematics Subject Classification

01-08 62H30 68M11 68P10 68U15 68W27 90C35 91C20 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Institute of Information Systems and MarketingKarlsruhe Institute of TechnologyKarlsruheGermany

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