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Visual Analytics for Understanding Texts

In: Visual Analytics for Data Scientists

Author

Listed:
  • Natalia Andrienko

    (Fraunhofer Institute Intelligent Analysis and Information Systems IAIS, Schloss Birlinghoven
    City, University of London, Northampton Square, Department of Computer Science)

  • Gennady Andrienko

    (Fraunhofer Institute Intelligent Analysis and Information Systems IAIS, Schloss Birlinghoven
    City, University of London, Northampton Square, Department of Computer Science)

  • Georg Fuchs

    (Fraunhofer Institute Intelligent Analysis and Information Systems IAIS, Schloss Birlinghoven)

  • Aidan Slingsby

    (City, University of London, Northampton Square, Department of Computer Science)

  • Cagatay Turkay

    (University of Warwick, Centre for Interdisciplinary Methodologies)

  • Stefan Wrobel

    (Fraunhofer Institute Intelligent Analysis and Information Systems IAIS, Schloss Birlinghoven
    University of Bonn)

Abstract

Texts are created for humans, who are trained to read and understand them. Texts are poorly suited for machine processing; still, humans need computer help when it is necessary to gain an overall understanding of characteristics and contents of large volumes of text or to find specific information in these volumes. Computer support in text analysis involves derivation of various kinds of structured data, such as numeric attributes and lists of significant items with associated numeric measures or weighted binary relationships between them. Computers themselves cannot give any meaning to the data they derive; therefore, the data need to be presented to humans in ways enabling semantic interpretations. While there exist a few text-specific visualisation techniques, such as Word Cloud and Word Tree, which explicitly represent words, it is often beneficial to use also general approaches suitable for multidimensional data. Collections of texts having spatial and/or temporal references are transformed to data that can be visualised and analysed using general methods devised for spatial, temporal, and spatio-temporal data.We show multiple examples of possible tasks in text data analysis and approaches to accomplishing them.

Suggested Citation

  • Natalia Andrienko & Gennady Andrienko & Georg Fuchs & Aidan Slingsby & Cagatay Turkay & Stefan Wrobel, 2020. "Visual Analytics for Understanding Texts," Springer Books, in: Visual Analytics for Data Scientists, chapter 0, pages 341-359, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-56146-8_11
    DOI: 10.1007/978-3-030-56146-8_11
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