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PDFWords — Co-occurrence network for VOSviewer

A bibliometric and text-mining tool that turns PDFs into word co-occurrence networks compatible with VOSviewer.

DOI

🔗 Live demo: text-umber-delta.vercel.app


About the project

PDFWords lets you load one or more PDF files, extract the text locally (with nothing sent to a server), process the words and generate a JSON file in the format accepted by VOSviewer Online. With that file you can visualise word co-occurrence maps in seconds.

It is especially useful for researchers carrying out bibliometric analyses, systematic literature reviews and text-mining studies.

For a directed, controlled-vocabulary approach (you define the terms and the app counts them), see the companion tool PDFTerms.


Features

  • Upload of multiple PDFs by drag & drop or selection
  • 100% local text extraction (no data sent anywhere)
  • Generation of a word co-occurrence network in the VOSviewer JSON format
  • Configurable parameters:
    • Minimum occurrence frequency
    • Minimum co-occurrence between terms
    • Maximum number of words in the network
    • Minimum character length per word
  • Automatic stopwords in Portuguese and English
  • Custom stopwords via the interface
  • Export of the VOSviewer JSON
  • Export of frequencies as CSV
  • Direct opening in VOSviewer Online
  • Responsive and accessible interface

How to use

  1. Go to text-umber-delta.vercel.app
  2. Drag your PDFs into the upload area (or click to select)
  3. Adjust the network parameters as needed
  4. Click Generate co-occurrence network
  5. Download the generated JSON
  6. Open app.vosviewer.com, click OpenVOSviewer JSON file and load the file

Note: scanned PDFs (image only) need OCR before processing.


Technologies

Technology Use
HTML / CSS / JavaScript Interface and application logic
PDF.js (v3.11) Text extraction from PDFs
VOSviewer Co-occurrence network visualisation
Vercel Hosting and continuous deployment

Privacy

All processing happens in the user's browser. No file or data is sent to external servers.


How to cite

Cunha, D. R. (2026). PDFWords: bibliometric text-mining for VOSviewer co-occurrence networks (v0.1.0) [Software]. Zenodo. https://doi.org/10.5281/zenodo.20708534


Author

Darliane Ribeiro Cunha, PhD Researcher in sustainability governance, environmental analytics and the SDGs ORCID: 0000-0003-2548-1237


Licence

This project is free to use for academic and research purposes.

About

PDFWords: bibliometric text-mining tool that turns PDFs into word co-occurrence networks for VOSviewer. 100% in-browser, no upload.

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