Visual Analysis of Text Annotations for Stance Classification with ALVA

ALVA is already being used by our domain experts in linguis1cs and computa1onal linguis1cs in order to improve the understanding of stance phenomena and to build a stance

We propose a Visual Analytics (VA) approach to address these challenges in the scope of human motion capture data, a special type of multivariate time series data.. In our

Our approach is a combination of automatic, interactive, and visual mechanisms that locally affect a base visualization and provide vi- sual feedback to help users carrying out

However, analysis of multiword topics requires systematic analysis to extract accurate topic results.. Therefore, we propose a visual system that accurate extracts topic results

Our approach builds on a simple model of human machine dialogue for interactive data analysis which we then propose to instantiate as visual analytics workflows – representations

They cover a variety of topics in Computer Graphics and Visual Computing, such as scientific visualization, visual data analysis, volumetric models visualization and

To support visual exploration of flow patterns, we employ advanced pattern mining methods based on k-nearest neighbor (k-NN) graphs. Our approach leverages a visual analysis pipeline

The work deals with a visual-auditory representation and an analysis of static and dynamic continuous scalar fields.. We propose a general approach and give examples of dynamic