科技创新发展战略研究

科技创新发展战略研究 ›› 2022, Vol. 6 ›› Issue (4): 1-12.

• 科技政策与法规 •    下一篇

基于自然语言处理与社会网络分析的国家创新驱动政策研究

黄雪梅1, 黄定轩2   

  1. 1. 重庆市规划设计研究院,重庆 401120;
    2. 重庆理工大学管理学院,重庆 400054
  • 收稿日期:2022-04-29 出版日期:2022-08-15 发布日期:2023-04-26
  • 通讯作者: 黄定轩(1970-),通信作者,男,研究员,博士,研究方向:服务管理与大数据应用管理。
  • 作者简介:黄雪梅(1972-),女,高级工程师,硕士,研究方向:区域经济与国土空间规划.
  • 基金资助:
    重庆市教育委员会人文社会科学研究项目“成渝地区双城经济圈先进制造业和生产性服务业深度融合发展研究”(21SKGH176)

Research on National Innovation Driven Policies Based on Natural Language Processing and Social Network Analysis

HUANG Xue-mei1, HUANG Ding-xuan2   

  1. 1. Chongqing Planing & Design Institute, Chongqing 401120, China;
    2. School of Management, Chongqing University of Technology, Chongqing 400054, China
  • Received:2022-04-29 Online:2022-08-15 Published:2023-04-26

摘要: 对国家创新驱动政策的精准理解有助于靶向定制不同区域的创新驱动政策,促进产业高质量发展。基于对我国的国家创新驱动政策量化分析的不足,采用自然语言处理技术与社会网络分析方法进行计量分析,结果表明:国家创新驱动政策文本的高频词社会网络具有内层和外层两个连接强度不同的层级,总体上以“创新”和“发展”为显著特征,这两个高频词之间具有最大的连接强度和影响力,不同高频词具有不同的影响力和社会网络特征。基于此,提出了不同地区制定创新驱动发展政策的管理启示。

关键词: 创新驱动政策, 自然语言处理, 社会网络分析

Abstract: An accurate understanding of national innovation driven policies can help to target and customize innovation-driven policies in different regions and promote high-quality industrial development. Based on the shortcomings of quantitative analysis of national innovation driven policies, this paper uses natural language processing technology and social network analysis methods for econometric analysis, the results show that the high-frequency word social network of national innovation driven policies has two inner and outer layers with different connection strength, national innovation driven policies are overall characterized by "innovation" and "development", these two high-frequency words have the greatest connection strength and influence, and different high-frequency words have different influences and social network characteristics. Based on this, the paper puts forward the management enlightenment of formulating innovation driven development policies in different regions.

Key words: innovation driven policy, natural language processing, social network analysis

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