GlassViz: Visualizing Automatically-Extracted Entry Points for Exploring Scientific Corpora in Problem-Driven Visualization Research

TL;DR

GlassViz is a visual text analytics tool that uses keyword associations derived from interdisciplinary research to facilitate exploration of large scientific corpora, especially in visualization for digital humanities.

Contribution

The paper introduces a novel model and proof-of-concept tool that automatically extracts entry points for document exploration based on interdisciplinary keyword analysis.

Findings

Effective visualization of keyword associations for corpus exploration

Demonstrated in visualization for digital humanities

Enhances document discovery in problem-driven visualization research

Abstract

In this paper, we report the development of a model and a proof-of-concept visual text analytics (VTA) tool to enhance documentdiscovery in a problem-driven visualization research (PDVR) con-text. The proposed model captures the cognitive model followed bydomain and visualization experts by analyzing the interdisciplinarycommunication channel as represented by keywords found in twodisjoint collections of research papers. High distributional inter-collection similarities are employed to build informative keywordassociations that serve as entry points to drive the exploration of alarge document corpus. Our approach is demonstrated in the contextof research on visualization for the digital humanities.