TextFlow: Towards Better Understanding of Evolving Topics in Text

By Weiwei Cui, Shixia Liu, Li Tan, Conglei Shi, Yangqiu Song, Zekai J. Gao, Huamin Qu, Xin Tong. 2011

426 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics

How researchers see this paper

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

In Artificial Intelligence

1. Shixia Liu
2. Jun Zhu
3. Mengchen Liu
4. Yingcai Wu
5. Chongxuan Li
6. Jiaxin Shi
7. Huamin Qu
8. Weiwei Cui

In Computer Vision and Pattern Recognition

1. Shixia Liu
2. Mengchen Liu
3. Yingcai Wu
4. Jun Zhu
5. Weiwei Cui
6. Chongxuan Li
7. Huamin Qu
8. Ronghua Liang

In Statistical and Nonlinear Physics

1. Shixia Liu
2. Mengchen Liu
3. Yingcai Wu
4. Weiwei Cui
5. Jiaxin Shi
6. Chongxuan Li
7. Jun Zhu
8. Jian Pei

Citations in the corpus

• Towards Better Analysis of Deep Convolutional Neural Networks (2016, methodology): “In CNNVis, we employ the layout algorithm in TextFlow [12, 13] to calculate the position of each node (e.”
• Visual Analysis of Topic Competition on Social Media (2013, methodology): “Cui et al. [9] introduced TextFlow to show the relationships among topics in text corpora.”
• A Survey of Visual Analytics Techniques and Applications: State-of-the-Art Research and Future Challenges (2013, background): “TextFlow([75]) integrates topic mining techniques into interactive visualizations to visually analyze the evo-”
• How Hierarchical Topics Evolve in Large Text Corpora (2014, background): “The state-of-the-art approach, TextFlow [10], assumes the evolving topics are flat.”
• EvoRiver: Visual Analysis of Topic Coopetition on Social Media (2014, methodology): “Both approaches [13, 44] employ a composite visual design that draws threads above the strips of a streamgraph.”
• UTOPIAN: User-Driven Topic Modeling Based on Interactive Nonnegative Matrix Factorization (2013, methodology): “Specifically, TextFlow handles merging and splitting topics over time as well as detects emerging/diminishing topics.”
• StoryFlow: Tracking the Evolution of Stories (2013, background): “A variety of specialized techniques based on the storyline visualization have been proposed for different applications, such as tracing generational relationships in genealogical data [20], tracking community evolution in dynamic networks [34], and illustrating topic merging/splitting relationships ”
• 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.”
• A survey on information visualization: recent advances and challenges (2014, methodology): “For example, TextFlow [31,46] was developed to illustrate topic merging/splitting relationships and their evolution in a text stream.”
• Online Visual Analytics of Text Streams (2015, background): “The visual analysis of evolving topics in text corpora has been widely studied in recent years [11], [19], [24], [32].”
• An Efficient Framework for Generating Storyline Visualizations from Streaming Data (2015, background): “The results show that our framework is able to not only The authors are with the VIDI Research Group, University California, Davis, CA. E-mail: ytanahashi@ucdavis.edu, javidhsueh@gmail.com, ma@cs.ucdavis.edu.”