Generation of Conceptual-Level Text Cloud with Graph Diffusion

In this paper, we explore a novel framework to generate a well-known text cloud visualization with the conceptual sense. The traditional text cloud is usually generated according to the word occurrence, possibly including the idf-based concept for word weight. The solution is applicable for the long articles. However, for a set of short sentences such as daily news titles, we cannot easily understand the weight of each keyword and its importance to users since the idf value and occurrence in short sentences are difficult to be both well discriminative. In this paper, we propose a graph-based diffusion model to generate conceptual level keyword cloud. We utilize the RDF-based Wikipedia word relation and apply in the Chinese news titles from different news sources. The result shows that our visualization can easily capture the importance concept revealed in a set of news titles.