信息资源管理学报 ›› 2023, Vol. 13 ›› Issue (1): 115-128.doi: 10.13365/j.jirm.2023.01.115

• 研究论文 • 上一篇    下一篇

中国政府在做什么——基于词汇关联的《政府工作报告》内容结构分析

胡吉明1  杨泽贤1 朱国伟2 文鹏2   

  1. 1. 武汉大学信息管理学院,武汉,430072;  
    2. 武汉大学马克思主义学院,武汉,430072
  • 出版日期:2023-01-26 发布日期:2023-03-18
  • 作者简介:胡吉明,博士,教授,博士生导师,研究方向为政务信息学;杨泽贤,硕士生,研究方向为政务信息学;朱国伟,博士,副教授,硕士生导师,研究方向为中国特色政府管理理论与实践;文鹏(通讯作者),博士,讲师,研究方向为信息资源管理与服务,Email: wenpeng@whu.edu.cn。
  • 基金资助:
    教育部人文社会科学基金规划项目(18YJA870004),湖北省青年拔尖人才培养计划项目。

What is the Chinese Government Doing: A Content Structure Network Analysis of Chinese Government Work Report Based on Word Correlation

Hu Jiming1 Yang Zexian1 Zhu Guowei2  Wen Peng2   

  1. 1. School of Information Management, Wuhan University; 
    2. School of Marxism, Wuhan University, Wuhan 430072
  • Online:2023-01-26 Published:2023-03-18

摘要: 从《报告》文本语句入手,基于词汇关联理论提出了政府工作内容结构的分析策略,以期直观和量化揭示我国政府工作的发展态势。通过提取《报告》文本中的主题词,构建主题词关联网络,计算整体和个体网络指标,可视化展示内容网络结构、发展脉络和态势,揭示政府工作的主要方向、重点领域及其相互作用结构和未来趋势。结果表明,近年来我国政府工作呈现出涉及面较广,统筹兼顾与稳步推进,规划性与回应性兼具的特征。政府工作在“规划”变动、政府换届中保持了稳定性和连续性,也呈现出对外在环境要求和任务变动的极强回应性。政府工作主要集中于10个方向和9个重点领域,且形成了具有持续性的两大脉络。综上,本研究对《报告》内容的深层次挖掘分析有效揭示了我国政府的工作内容、结构特征以及发展态势。

关键词: 政府工作, 《政府工作报告》, 主题关联网络, 共词分析, 可视化

Abstract: Taking sentence in reports as analysis unit, this paper proposed a new research framework that can analyze the content structure of government work report based on word correlation in order to visually and quantitatively reveal the development of government works in our country. This paper chose Chinese government work reports as essential data, and extracted subject words from the text. Correlation networks at various levels were constructed according to co-occurrence relationship between pairs of words, and then indicators of the whole and individual network were calculated. In addition, the network structure, the evolution venation and trends were visualized to reveal main directions of government works, key fields, as well as their interactive structures and future trends. Results indicate that Chinese government work covered a wide range of fields during the past years. It is promoted steadily in a holistic approach, taking programmatic and responsive characteristics into consideration. Chinese government work remained consistent and continuous in changes of plans and the central government rotation, as well as greatly responsive to external environment and changes of tasks. Chinese government work mainly concentrated on 10 directions and 9 key fields, and there were two large-scale evolution venations. In conclusion, this paper effectively reveals connotations, structural characteristics, and development trends of government work through deep text mining of Chinese government work reports.

Key words: Government work, Government work report, Subject correlation network, Co-word analysis, Visualization

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