Online Visual Analytics of Text Streams

By Shixia Liu, Jialun Yin, Xiting Wang, Weiwei Cui, Kelei Cao, Jian Pei. 2015

82 citations recorded by the source corpus.

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.

Subfields: Computer Vision and Pattern Recognition, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Jun Zhu
2. Chongxuan Li
3. Mengchen Liu
4. Jiaxin Shi
5. Shixia Liu
6. Xiting Wang
7. Z. Li
8. Alfredo Vellido

In Signal Processing

1. Jun Zhu
2. Mengchen Liu
3. Z. Li
4. Jiaxin Shi
5. Xiting Wang
6. Chongxuan Li
7. Shixia Liu
8. Alfredo Vellido

Citations in the corpus

• Towards Better Analysis of Deep Convolutional Neural Networks (2016, methodology): “To address this issue, we improved existing DAG layout methods [18, 40, 52, 55, 57].”
• Towards better analysis of machine learning models: A visual analytics perspective (2017, background): “In addition to an explosion of research on better understanding of learning results [6, 7, 8, 9, 10, 11, 12, 13, 14], researchers have paid increasing attention to leveraging interactive visualizations to better understand and iteratively improve a machine learning model.”