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In this study, regional (cities, towns and villages) data and tweet data areobtained from Twitter, and extract information of purchase information (Whereand what bought) from the tweet data by morphological analysis and rule-baseddependency analysis. Then, the "The regional information" and "The informationof purchase history (Where and what bought information)" are captured asbipartite graph, and Responsiveness Pair Clustering analysis (a clusteringusing correspondence analysis as similarity measure) is conducted. In thisstudy, since it was found to be difficult to analyze a network such asbipartite graph having limitations in links by using modularity Q,responsiveness is used instead of modularity Q as similarity measure. As aresult of this analysis, "regional information cluster" which refers to similar"The information of purchase history" nodes group is generated. Finally,similar regions are visualized by mapping the regional information cluster onthe map. This visualization system is expected to contribute as an analyticaltool for customers purchasing behavior and so on.

The Study about the Analysis of Responsiveness Pair Clustering to Social Network Bipartite Graph