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Applying Text Mining, Clustering Analysis, and Latent Dirichlet Allocation Techniques for Topic Classification of Environmental Education Journals

Author

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  • I-Cheng Chang

    (Department of Environmental Engineering, National Ilan University, Yilan 260, Taiwan)

  • Tai-Kuei Yu

    (Department of Business Administration, National Quemoy University, Kinmen 892, Taiwan)

  • Yu-Jie Chang

    (Department of Earth and Life Science, University of Taipei, Taipei 100, Taiwan)

  • Tai-Yi Yu

    (Department of Risk Management and Insurance, Ming Chuan University, Taipei 111, Taiwan)

Abstract

Facing the big data wave, this study applied artificial intelligence to cite knowledge and find a feasible process to play a crucial role in supplying innovative value in environmental education. Intelligence agents of artificial intelligence and natural language processing (NLP) are two key areas leading the trend in artificial intelligence; this research adopted NLP to analyze the research topics of environmental education research journals in the Web of Science (WoS) database during 2011–2020 and interpret the categories and characteristics of abstracts for environmental education papers. The corpus data were selected from abstracts and keywords of research journal papers, which were analyzed with text mining, cluster analysis, latent Dirichlet allocation (LDA), and co-word analysis methods. The decisions regarding the classification of feature words were determined and reviewed by domain experts, and the associated TF-IDF weights were calculated for the following cluster analysis, which involved a combination of hierarchical clustering and K-means analysis. The hierarchical clustering and LDA decided the number of required categories as seven, and the K-means cluster analysis classified the overall documents into seven categories. This study utilized co-word analysis to check the suitability of the K-means classification, analyzed the terms with high TF-IDF wights for distinct K-means groups, and examined the terms for different topics with the LDA technique. A comparison of the results demonstrated that most categories that were recognized with K-means and LDA methods were the same and shared similar words; however, two categories had slight differences. The involvement of field experts assisted with the consistency and correctness of the classified topics and documents.

Suggested Citation

  • I-Cheng Chang & Tai-Kuei Yu & Yu-Jie Chang & Tai-Yi Yu, 2021. "Applying Text Mining, Clustering Analysis, and Latent Dirichlet Allocation Techniques for Topic Classification of Environmental Education Journals," Sustainability, MDPI, vol. 13(19), pages 1-20, September.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:19:p:10856-:d:646836
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    References listed on IDEAS

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    Cited by:

    1. Vrdoljak Ivana, 2023. "Lifelong Education in Economics, Business and Management Research: Literature Review," Business Systems Research, Sciendo, vol. 14(1), pages 153-172, September.
    2. Mini Zhu & Gang Wang & Chaoping Li & Hongjun Wang & Bin Zhang, 2023. "Artificial Intelligence Classification Model for Modern Chinese Poetry in Education," Sustainability, MDPI, vol. 15(6), pages 1-19, March.
    3. Christoph Funk & Elena Tönjes & Ramona Teuber & Lutz Breuer, 2024. "Reading between the lines: The intersection of research attention and sustainable development goals," Sustainable Development, John Wiley & Sons, Ltd., vol. 32(5), pages 4545-4566, October.

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