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Adding Taxonomies Obtained by Content Clustering to Semantic Social Network Analysis

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Part of the Advances in Intelligent and Soft Computing book series (AINSC,volume 76)

Abstract

This paper introduces a novel method to analyze the content of communication in social networks. Content clustering methods are used to extract a taxonomy of concepts from each analyzed communication archive. Those taxonomies are hierarchical categorizations of the concepts discussed in the analyzed communication archives. Concepts are based on terms extracted from the communication’s content. The resulting taxonomy provides insights into the communication not possible through conventional social network analysis.

Keywords

  • Social Network
  • Information Retrieval
  • Semantic Similarity
  • Social Network Analysis
  • Betweenness Centrality

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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Fuehres, H., Fischbach, K., Gloor, P.A., Krauss, J., Nann, S. (2010). Adding Taxonomies Obtained by Content Clustering to Semantic Social Network Analysis. In: Bastiaens, T.J., Baumöl, U., Krämer, B.J. (eds) On Collective Intelligence. Advances in Intelligent and Soft Computing, vol 76. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14481-3_11

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  • DOI: https://doi.org/10.1007/978-3-642-14481-3_11

  • Publisher Name: Springer, Berlin, Heidelberg

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