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Analytical semantics visualization for discovering latent signals in large text collections

By Christian Stab, Matthias Breyer, Dirk Burkhardt, Kawa Nazemi and Jörn Kohlhammer

Abstract

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

Topics: semantic visualization, trend analysis, information visualization, data mining, business intelligence
Year: 2012
OAI identifier: oai:fraunhofer.de:N-225837
Provided by: Fraunhofer-ePrints
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