基于多视图协同的政策文本可视化研究

Visualizing Policy Texts Based on Multi-View Collaboration

【目的】通过多视图协同可视化的方式对时序文本挖掘过程进行可视化,以实现多角度多层面洞察文本数据背后隐藏的规律和信息。【方法】基于文本词向量矩阵,对多政策主体的政策时序文本,通过数据清洗、计算TF-IDF值、构建向量空间模型、奇异值分解等文本挖掘技术进行信息提取和可交互的可视化探索分析。【结果】选取2016年1月–2017年8月北京市中关村示范区下属子园区的相关委内信息文本为样本,采用文本挖掘技术和多视图协同可视化方法,探索政策文本背后的规律,验证了本文方法和框架的有效性。【局限】针对大规模文本具体细节单数据点的展示效果不佳,文本挖掘技术有待进一步提升,模型数据吞吐量有待基于大数据架构进一步增强。【结论】本文方法和框架能够对时序文本类数据做到充分的挖掘展示,更好地把握数据背后的信息,增强政策主体的决策依据。

[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.