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<p>Polymer science has enhanced human life for more than 100 years, and numerous scientific papers have been published in this field. Although reviewing overall trends is valuable, manually processing such a large volume of information is difficult. In this study, we captured trends in polymer science by performing an automated analysis of papers using topic-modeling techniques grounded in natural language processing (NLP). We analyzed the titles and abstracts of papers that contained the keyword “polymer” in their titles and were published from 1991–2023, applying latent Dirichlet allocation (LDA), singular value decomposition (SVD), and nonnegative matrix factorization (NMF) as topic models. This research showed that LDA, SVD, and NMF can capture trends across multiple fields over the past three decades. Accordingly, NLP-based topic models are promising tools for automatically extracting useful information from papers and other textual data in polymer science.</p>

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Natural language processing-based topic models for analyzing trends in polymer science

  • 期刊论文

  • 发表日期：

    2025年5月29日

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  • Yoshifumi Amamoto,
  • Yoh-ichi Mototake,
  • Takaaki Ohnishi

摘要

Polymer science has enhanced human life for more than 100 years, and numerous scientific papers have been published in this field. Although reviewing overall trends is valuable, manually processing such a large volume of information is difficult. In this study, we captured trends in polymer science by performing an automated analysis of papers using topic-modeling techniques grounded in natural language processing (NLP). We analyzed the titles and abstracts of papers that contained the keyword “polymer” in their titles and were published from 1991–2023, applying latent Dirichlet allocation (LDA), singular value decomposition (SVD), and nonnegative matrix factorization (NMF) as topic models. This research showed that LDA, SVD, and NMF can capture trends across multiple fields over the past three decades. Accordingly, NLP-based topic models are promising tools for automatically extracting useful information from papers and other textual data in polymer science.