Online Visual Analytics of Text Streams
作者： Shixia Liu , Jialun Yin , Xiting Wang , Weiwei Cui , Kelei Cao
DOI: 10.1109/TVCG.2015.2509990
关键词:
摘要: 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 is identify representative topics incoming documents align them with the existing that they immediately follow (in time). To end, we learn a set of streaming tree cuts from trees based on user-selected focus nodes. A dynamic Bayesian network model has been developed derive balance fitness each cut smoothness between adjacent cuts. By connecting corresponding at different times, are able provide overview evolving topics. sedimentation-based visualization designed enable interactive analysis data global patterns local details. evaluated our method real-world datasets results generally favorable.
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