In this study, regional (cities, towns and villages) data and tweet data are obtained from Twitter, and extract information of purchase information (Where and what bought) from the tweet data by morphological analysis and rule-based dependency analysis. Then, the "The regional information" and "The information of purchase history (Where and what bought information)" are captured as bipartite graph, and Responsiveness Pair Clustering analysis (a clustering using correspondence analysis as similarity measure) is conducted. In this study, since it was found to be difficult to analyze a network such as bipartite graph having limitations in links by using modularity Q, responsiveness is used instead of modularity Q as similarity measure. As a result 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 on the map. This visualization system is expected to contribute as an analytical tool for customers purchasing behavior and so on.