Article | Proceedings of SIGRAD 2012; Interactive Visual Analysis of Data; November 29-30; 2012; Växjö; Sweden | Analytical Semantics Visualization for Discovering Latent Signals in Large Text Collections

Title:
Analytical Semantics Visualization for Discovering Latent Signals in Large Text Collections
Author:
Christian Stab: Fraunhofer Institute for Computer Graphics Research IGD, Germany Matthias Breyer: Fraunhofer Institute for Computer Graphics Research IGD, Germany Dirk Burkhardt: Fraunhofer Institute for Computer Graphics Research IGD, Germany Kawa Nazemi: Fraunhofer Institute for Computer Graphics Research IGD, Germany Jörn Kohlhammer: Fraunhofer Institute for Computer Graphics Research IGD, Germany
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Year:
2012
Conference:
Proceedings of SIGRAD 2012; Interactive Visual Analysis of Data; November 29-30; 2012; Växjö; Sweden
Issue:
081
Article no.:
011
Pages:
83-86
No. of pages:
4
Publication type:
Abstract and Fulltext
Published:
2012-11-20
ISBN:
978-91-7519-723-4
Series:
Linköping Electronic Conference Proceedings
ISSN (print):
1650-3686
ISSN (online):
1650-3740
Publisher:
Linköping University Electronic Press; Linköpings universitet


Considering the increasing pressure of competition and high dynamics of markets; the early identification and specific handling of novel developments and trends becomes more and more important for competitive companies. Today; those signals are encoded in large amounts of textual data like competitors’ web sites; news articles; scientific publications or blog entries which are freely available in the web. Processing large amounts of textual data is still a tremendous challenge for current business analysts and strategic decision makers. Although current information systems are able to process that amount of data and provide a wide range of information retrieval tools; it is almost impossible to keep track of each thread or opportunity. The presented approach combines semantic search and data mining techniques with interactive visualizations for analyzing and identifying weak signals in large text collections. Beside visual summarization tools; it includes an enhanced trend visualization that supports analysts in identifying latent topic-related relations between competitors and their temporal relevance. It includes a graph-based visualization tool for representing relations identified during semantic analysis. The interaction design allows analysts to verify their retrieved hypothesis by exploring the documents that are responsible for the current view.

Keywords: H.5.2 [Information Interfaces and Presentations]: User Interfaces—Graphical user interfaces (GUI); Interaction styles H.3.1 [Information Storage and Retrieval]: Content Analysis and Indexing—Abstracting methods

Proceedings of SIGRAD 2012; Interactive Visual Analysis of Data; November 29-30; 2012; Växjö; Sweden

Author:
Christian Stab, Matthias Breyer, Dirk Burkhardt, Kawa Nazemi, Jörn Kohlhammer
Title:
Analytical Semantics Visualization for Discovering Latent Signals in Large Text Collections
References:

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Proceedings of SIGRAD 2012; Interactive Visual Analysis of Data; November 29-30; 2012; Växjö; Sweden

Author:
Christian Stab, Matthias Breyer, Dirk Burkhardt, Kawa Nazemi, Jörn Kohlhammer
Title:
Analytical Semantics Visualization for Discovering Latent Signals in Large Text Collections
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