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This paper proposes an approach on a method for visual text analytics to support knowledge building, analytical reasoning and explorative analysis. For this purpose we use semantic network models that are automatically retrieved from unstructured text data using a parametric k-next-neighborhood model. Semantic networks are analyzed with methods of network analysis to gain quantitative and qualitative insights. Quantitative metrics can support the qualitative analysis and exploration of semantic structures. We discuss theoretical presuppositions regarding the text modeling with semantic networks to provide a basis for subsequent semantic network analysis. By presenting a systematic overview of basic network elements and their qualitative meaning for semantic network analysis, we describe exploration strategies that can support analysts to make sense of a given network. As a proof of concept, we illustrate the proposed method by an exemplary analysis of a wikipedia article using a visual text analytics system that leverages semantic network visualization for exploration and analysis.
Philipp Drieger (Sat,) studied this question.