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Data Analysis and Knowledge Discovery  2019, Vol. 3 Issue (6): 30-41    DOI: 10.11925/infotech.2096-3467.2018.0827
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Visualizing Policy Texts Based on Multi-View Collaboration
Yanan Yang1,3(),Wenhui Zhao2,Jian Zhang1,3,Shen Tan1,3,Beibei Zhang1,3
1(School of Economics and Management, Beijing University of Information Science and Technology, Beijing 100192, China)
2(School of Management and Economics, Beijing Institute of Technology, Beijing 100081, China)
3(Beijing Key Laboratory of Big Data Decision Making for Green Development, Beijing 100192, China)
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[Objective] This paper visualizes the text mining process through multi-view collaborative technique, aiming to identify the patterns and insights more effectively. [Methods] Based on the textual word vector matrix, we processed the texts of multi-policy subjects with data cleaning, TF-IDF calculation, vector space model, singular value decomposition and other methods. [Results] We examined effectivenesss of the proposed model with governmental information from Zhongguancun of Beijing during the period of January 2016 to August 2017. [Limitations] The framework could not visualize the single data points of large-scale texts. [Conclusions] Multi-view collaborative visualization is an effective way to interpretate text message.

Key wordsText Mining      Text Visualization      Multi-View Collaboration      Policy Analysis     
Received: 25 July 2018      Published: 15 August 2019

Cite this article:

Yanan Yang,Wenhui Zhao,Jian Zhang,Shen Tan,Beibei Zhang. Visualizing Policy Texts Based on Multi-View Collaboration. Data Analysis and Knowledge Discovery, 2019, 3(6): 30-41.

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[1] 姜婷婷, 肖卫东, 张翀, 等. 基于桑基图的时间序列文本可视化方法[J]. 计算机应用研究, 2016, 33(9): 2683-2687.
[1] (Jiang Tingting, Xiao Weidong, Zhang Chong, et al.Text Visualization Method for Time Series Based on Sankey Diagram[J]. Application Research of Computers, 2016, 33(9): 2683-2687.)
[2] Wise J A, Thomas J J, Pennock K, et al.Visualizing the Non-Visual: Spatial Analysis and Interaction with Information from Text Documents[C]// Proceedings of the IEEE Symposium on Information Visualization. 1995: 51.
[3] 余红梅, 梁战平. 文本可视化技术与竞争情报[J]. 图书情报工作, 2011, 55(8): 79-83.
[3] (Yu Hongmei, Liang Zhanping.Text Visualization Technologies and Competitive Intelligence[J]. Library and Information Service, 2011, 55(8): 79-83.)
[4] 唐家渝, 刘知远, 孙茂松. 文本可视化研究综述[J]. 计算机辅助设计与图形学学报, 2013, 25(3): 273-285.
[4] (Tang Jiayu, Liu Zhiyuan, Sun Maosong.A Survey of Text Visualization[J]. Journal of Computer-Aided Design & Computer Graphics, 2013, 25(3): 273-285.)
[5] 张伟. 可视化分析技术在网络舆情研究中的应用[J]. 现代情报, 2016, 36(11): 82-86.
[5] (Zhang Wei.The Application of Visualization in Network Public Opinion Research[J]. Journal of Modern Information, 2016, 36(11): 82-86.)
[6] Lin C Y, Li T Y, Chen P. An Information Visualization System to Assist News Topics Exploration with Social Media[C]// Proceedings of the 7th 2016 International Conference on Social Media & Society. ACM, 2016: Article No.23.
[7] 刘芳. 信息可视化技术及应用研究[D]. 杭州: 浙江大学, 2013.
[7] (Liu Fang.Information Visualization Technology and Application Research[D]. Hangzhou: Zhejiang University, 2013.)
[8] 王璟, 夏培玲. 基于Web of Science的国际体育政策研究热点可视化分析[J]. 沈阳体育学院学报, 2013, 32(1):32-36.
[8] (Wang Jing, Xia Peiling .Visualization Analysis of Research Frontier and Hot Topics About International Sport Policy Based on Web of Science[J]. Journal of Shenyang Sport University, 2013, 32(1): 32-36.)
[9] 韩永君. 国外体育政策研究演进的可视化分析[J]. 上海体育学院学报, 2017, 41(2): 7-14.
[9] (Han Yongjun.The Visualization Analysis on the Evolution of Foreign Sport Policy Research[J]. Journal of Shanghai University of Sport, 2017, 41(2): 7-14.)
[10] 朱皆笑. 教育治理现代化研究热点及政策演进——基于SATI的可视化分析[J]. 教育科学研究, 2017(3):56-62.
[10] (Zhu Jiexiao.Research Hotspots and Policy Evolution of Education Governance Modernization——Visual Analysis Based on SATI[J]. Educational Science Research, 2017(3):56-62.
[11] 吴佳鑫. Web环境下信息可视化模型研究[D]. 武汉: 武汉大学, 2009.
[11] (Wu Jiaxin.Study on Information Visualization Model for World Wide Web[D]. Wuhan: Wuhan University, 2009.)
[12] 陈谊, 蔡进峰, 石耀斌, 等. 基于平行坐标的多视图协同可视分析方法[J]. 系统仿真学报, 2013, 25(1): 81-86.
[12] (Chen Yi, Cai Jinfeng, Shi Yaobin, et al.Coordinated Visual Analytics Method Based on Multiple Views with Parallel Coordinates[J]. Journal of System Simulation, 2013, 25(1): 81-86.)
[13] 胡华全, 吴玲达, 杨超, 等. 卫星时变拓扑网络多视图可视化分析框架[J]. 系统工程与电子技术, 2014, 36(2): 312-316.
[13] (Hu Huaquan, Wu Lingda, Yang Chao, et al.Multiple-View Framework of Visual Analytics for Time-Varying Satellite Topology Network[J]. Systems Engineering and Electronics, 2014, 36(2): 312-316.)
[14] 刘明超, 吴升, 余劲松弟. 基于多视图协同的时空数据可视分析方法[J]. 测绘科学技术学报, 2017, 34(2): 211-214.
[14] (Liu Mingchao, Wu Sheng, Yu Jinsongdi.Spatio-Temporal Data Visual Analysis Method Based on Multiple-View Collaboration[J]. Journal of Geomatics Science and Technology, 2017, 34(2): 211-214.)
[15] 施聪莺, 徐朝军, 杨晓江, 等. TFIDF算法研究综述[J]. 计算机应用, 2009, 29(S1): 167-170, 180.
[15] (Shi Congying, Xu Chaojun, Yang Xiaojiang, et al.Study of TFIDF Algorithm[J]. Journal of Computer Applications, 2009, 29(S1): 167-170, 180.)
[16] Torgerson W S.Multidimensional Scaling: I. Theory and Method[J]. Psychometrika, 1952, 17(4): 401-419.
[17] Kalman D.A Singularly Valuable Decomposition: The SVD of a Matrix[J]. College Mathematics Journal, 1996, 27(1): 2-23.
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