Tallennettuna:
Bibliografiset tiedot
Päätekijät: Omogbene, Temitope Olorunyomi, Gebashe, Fikisiwe Cynthia, Lawal, Ibraheem Oduola, Amoo, Stephen Oluwaseun, Aremu, Adeyemi Oladapo
Aineistotyyppi: Recurso digital
Kieli:
Julkaistu: Zenodo 2025
Aiheet:
Linkit:https://doi.org/10.5281/zenodo.17385016
Tagit: Lisää tagi
Ei tageja, Lisää ensimmäinen tagi!
Sisällysluettelo:
  • <p>This collection includes <strong>Python notebooks</strong> (optimised for <strong>Google Colab</strong>) implementing machine learning and natural language processing (NLP) methods for advanced and complex bibliometric data analysis. The scripts automate keyword normalisation, thematic clustering, and topic modelling using <em>spaCy</em>, <em>scikit-learn</em>, and <em>NLTK</em>. Outputs include structured data suitable for bibliometric visualisation and network interpretation. The workflow enhances bibliometric insights by integrating semantic analysis and unsupervised learning, supporting studies in phytochemistry, metabolomics, ethnopharmacology, and related knowledge domains.</p>