Visualized Technique for Trend Analysis of News Articles

Authors:
Masahiro Terachi;Ryosuke Saga;Zhongqi Sheng;Hiroshi Tsuji
Affiliations:
Graduate School of Engineering, Osaka Prefecture University, Japan 559-8531;Graduate School of Engineering, Osaka Prefecture University, Japan 559-8531;Graduate School of Engineering, Osaka Prefecture University, Japan 559-8531 and School of Mechanical Engineering, Northeastern University, Liaoning, China 110004;Graduate School of Engineering, Osaka Prefecture University, Japan 559-8531
Venue:
IEA/AIE '08 Proceedings of the 21st international conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: New Frontiers in Applied Artificial Intelligence
Year:
2008

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Abstract

In order to visualize keyword trends in texts of news articles, this paper proposes a method named FACT-Graph by extending co-occurrence graph. The method uses four classes of keywords, considers three patterns of class transitions, and expresses three types of co-occurrence relationships between two analysis periods. Classes of keywords are characterized by the shapes of their nodes, the transition patterns of keyword classes are shown by the colors of the nodes, and the co-occurrences relationships between two keywords are represented by the types of edges their nodes have. FACT-Graph is applied to a sample of 220,000 newspaper articles and is found to be effective in visualizing keyword trends embedded in volumes of text data.