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Visual Analysis and Knowledge Discovery for Text

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Large-Scale Data Analytics

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Abstract

Providing means for effectively accessing and exploring large textual data sets is a problem attracting the attention of text mining and information visualization experts alike. The rapid growth of the data volume and heterogeneity, as well as the richness of metadata and the dynamic nature of text repositories, add to the complexity of the task. This chapter provides an overview of data visualization methods for gaining insight into large, heterogeneous, dynamic textual data sets. We argue that visual analysis, in combination with automatic knowledge discovery methods, provides several advantages. Besides introducing human knowledge and visual pattern recognition into the analytical process, it provides the possibility to improve the performance of automatic methods through user feedback.

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Author information

Authors and Affiliations

  1. University of Passau, 94030, Passau, Germany

    Christin Seifert & Michael Granitzer

  2. Know-Center Graz, Inffeldgasse 13/6, A-8010, Graz, Austria

    Vedran Sabol, Wolfgang Kienreich & Elisabeth Lex

Authors
  1. Christin Seifert
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  2. Vedran Sabol
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  3. Wolfgang Kienreich
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  4. Elisabeth Lex
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  5. Michael Granitzer
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Correspondence to Christin Seifert .

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Editors and Affiliations

  1. IBM Research - Ireland, Mulhuddart, Ireland

    Aris Gkoulalas-Divanis

  2. IBM Research - Zurich, Rüschlikon, Switzerland

    Abderrahim Labbi

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Seifert, C., Sabol, V., Kienreich, W., Lex, E., Granitzer, M. (2014). Visual Analysis and Knowledge Discovery for Text. In: Gkoulalas-Divanis, A., Labbi, A. (eds) Large-Scale Data Analytics. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-9242-9_7

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  • DOI: https://doi.org/10.1007/978-1-4614-9242-9_7

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