A Tool for Visualizing Topic Evolution in Large Text Collections
Feipeng Sun, Yanyan Li, Zhiqiang Zhang · 2013
Topic evolution in text data has become a flourishing frontier in the text mining community. Yet with the increasing number of texts, it is important and challenging to understand how topics evolve. In this paper, we introduce a tool to analyze various evolution patterns that emerge from multiple texts based on combination of topic modeling and visualization techniques. By mining topic hierarchical relationship and evolutionary trend, the tool provides three visualization views along with interactive functionality that enables users to understand the topic evolution in a flexible and easily way. Experiment on real dataset has shown that the developed tool is effective to visualizing meaningful topic evolution in large text collections.