Visual Analysis and Knowledge Discovery for Text

Christin Seifert; Vedran Sabol; Wolfgang Kienreich; Elisabeth Lex; Michael Granitzer

doi:10.1007/978-1-4614-9242-9_7

Visual Analysis and Knowledge Discovery for Text

Christin Seifert^*, Vedran Sabol, Wolfgang Kienreich, Elisabeth Lex, Michael Granitzer

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Chapter › Academic › peer-review

8 Citations (Scopus)

22 Downloads (Pure)

Abstract

Providing means for effectively accessing and exploring large textual data sets is a problem attracting attention of text mining and information visualization experts alike. Rapid growth of the data volume, heterogeneity and richness of metadata, and the dynamic nature of text repositories add to the complexity of the task. This chapter provides an overview of 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.

Original language	English
Title of host publication	Large Scale Data Analytics
Editors	Aris Gkoulalas-Divanis, Abderrahim Labbi
Place of Publication	New York, NY
Publisher	Springer
Pages	189-218
Number of pages	30
ISBN (Electronic)	978-1-4614-9242-9
ISBN (Print)	978-1-4614-9241-2
DOIs	https://doi.org/10.1007/978-1-4614-9242-9_7
Publication status	Published - 2014
Externally published	Yes

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10.1007/978-1-4614-9242-9_7

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AU - Seifert, Christin

AU - Sabol, Vedran

AU - Kienreich, Wolfgang

AU - Lex, Elisabeth

AU - Granitzer, Michael

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AB - Providing means for effectively accessing and exploring large textual data sets is a problem attracting attention of text mining and information visualization experts alike. Rapid growth of the data volume, heterogeneity and richness of metadata, and the dynamic nature of text repositories add to the complexity of the task. This chapter provides an overview of 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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Visual Analysis and Knowledge Discovery for Text

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