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Policy analysis combining artificial intelligence and text mining technology in the perspective of educational informatization

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  • Han Kuang

    (Central China Normal University)

  • Peng Tian

    (Central China Normal University)

  • Xiuwei Liang

    (Central China Normal University)

Abstract

In order to explore the application potential of artificial intelligence (AI) and text mining technology in educational policy analysis and evaluate their impact on the psychological perception of policy audiences, this study firstly introduces the application of AI and text mining technology in education. Secondly, it explores the application of psychological theories in educational policy analysis. Finally, this study constructs an educational policy text analysis model and verifies the feasibility of the optimized model through performance comparison experiments and case analysis. The experimental results show that the optimized model exhibits higher accuracy, recall rate, and F1 score compared to traditional models when handling educational policy text analysis tasks with different data volumes. This finding highlights the importance of optimizing models for specific tasks and the potential of improving the understanding and analysis capabilities of models for specific text types through careful adjustments. In addition, the application of psychological theories to the analysis of educational policy texts provides a new perspective and method for understanding the impact of policies on audience psychological states, which helps in formulating more effective and humanized policies. Therefore, the study has certain reference significance for the use of AI and text mining technology to support educational policy analysis and formulation, providing valuable insights and guidance for future related research and practice.

Suggested Citation

  • Han Kuang & Peng Tian & Xiuwei Liang, 2024. "Policy analysis combining artificial intelligence and text mining technology in the perspective of educational informatization," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-9, December.
    • Handle: RePEc:pal:palcom:v:11:y:2024:i:1:d:10.1057_s41599-024-04076-0
    DOI: 10.1057/s41599-024-04076-0
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    References listed on IDEAS

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    1. Marina Johnson & Rashmi Jain & Peggy Brennan-Tonetta & Ethne Swartz & Deborah Silver & Jessica Paolini & Stanislav Mamonov & Chelsey Hill, 2021. "Impact of Big Data and Artificial Intelligence on Industry: Developing a Workforce Roadmap for a Data Driven Economy," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 22(3), pages 197-217, September.
    2. Goodell, John W. & Kumar, Satish & Lim, Weng Marc & Pattnaik, Debidutta, 2021. "Artificial intelligence and machine learning in finance: Identifying foundations, themes, and research clusters from bibliometric analysis," Journal of Behavioral and Experimental Finance, Elsevier, vol. 32(C).
    3. Giacomo Damioli & Vincent Van Roy & Daniel Vertesy, 2021. "The impact of artificial intelligence on labor productivity," Eurasian Business Review, Springer;Eurasia Business and Economics Society, vol. 11(1), pages 1-25, March.
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