Online Visual Analytics of Text Streams
This work addresses the challenge for users needing to analyze evolving topics in streaming text data, representing an incremental improvement in visual analytics methods.
The paper tackled the problem of exploring hierarchical topic evolution in high-volume text streams by developing an online visual analytics approach that identifies and aligns representative topics over time, with evaluation on real-world datasets showing generally favorable results.
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.