Generation of Conceptual-Level Text Cloud with Graph Di usion

Generation of Conceptual-Level Text Cloud with Graph Di usion

In this paper, we explore a novel framework to generate a well-known text cloud visualiza- tion 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 impor- tance to users since the idf value and occurrence in short sentences are di cult to be both well discriminative. In this paper, we propose a graph-based di usion model to generate conceptual level keyword cloud. We utilize the RDF-based Wikipedia word relation and apply in the Chinese news titles from di erent news sources. The result shows that our visualization can easily capture the importance concept revealed in a set of news titles.