Online Visual Analytics of Text Streams

TL;DR

This paper introduces an online visual analytics system for exploring hierarchical topic evolution in high-volume text streams, combining streaming tree cuts, a dynamic Bayesian network, and sedimentation visualization for interactive analysis.

Contribution

It proposes a novel approach that learns streaming tree cuts using a dynamic Bayesian network to track hierarchical topic changes over time in streaming text data.

Findings

Effective in real-world datasets

Provides smooth hierarchical topic evolution visualization

Facilitates interactive exploration of text streams

Abstract

We present an online visual analytics approach to helping users explore and understand hierarchical topic evolution in high-volume text streams. The key idea behind this approach is to identify representative topics in incoming documents and align them with the existing representative topics that they immediately follow (in time). To this end, we learn a set of streaming tree cuts from topic trees based on user-selected focus nodes. A dynamic Bayesian network model has been developed to derive the tree cuts in the incoming topic trees to balance the fitness of each tree cut and the smoothness between adjacent tree cuts. By connecting the corresponding topics at different times, we are able to provide an overview of the evolving hierarchical topics. A sedimentation-based visualization has been designed to enable the interactive analysis of streaming text data from global patterns to local details. We evaluated our method on real-world datasets and the results are generally favorable.