TextInsight: A new text visualization system based on entropy and GMap

In recent years, text visualization has been widely acknowledged as an effective approach for understanding the structure and patterns hidden in complicated textual information. In this paper, we propose a new visualization system called TextInsight with two of our contributions. Firstly, a textual entropy theory is introduced to encode the semantic importance distribution in the corpus. Based on the proposed multidimensional joint probability histogram in vector fields, the improved algorithm provides a novel way to position valuable information in massive short texts accurately. Secondly, a map-like metaphor is generated to visualize the textual topics and their relationships. For the problem of over-segmentation in the layout and clustering procedure, we propose an optimization algorithm combining Affinity Propagation (AP) and MultiDimensional Scaling (MDS), and the improved geographical representation is more comprehensible and aesthetically appealing. Our experimental results and initial user feedback suggest that this system is effective in aiding text analysis.