  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.
