Scientometric Analysis of Quantum Natural Language Processing (QNLP)

This repository contains the code, data processing scripts, and analysis pipelines used in the article:

A Scientometric Review of Practical Applications in Quantum Natural Language Processing (QNLP): Trends, Gaps, and Research Opportunities Victor R. Silva et al., IEEE Access, 2025

The goal of this repository is to ensure reproducibility, transparency, and extensibility of the scientometric study, enabling other researchers to replicate the analyses, adapt the pipeline to other domains, or extend the results with new data.

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📌 Overview

Quantum Natural Language Processing (QNLP) is an emerging interdisciplinary field at the intersection of quantum computing, machine learning, and natural language processing. Despite rapid growth, research remains fragmented.

This repository supports a large-scale scientometric and bibliometric analysis of QNLP literature, including:

• Temporal evolution of publications
• Author productivity and collaboration networks
• Core journals and sources (Bradford’s Law)
• Country-level scientific production
• Keyword co-occurrence, thematic maps, and research gaps
• Identification of underexplored application domains (e.g., healthcare)

The analyses follow PRISMA guidelines and rely on data collected from Scopus and Web of Science (2014–2024).

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📂 Repository Structure

qnlp-scientometrics/
├── LICENSE
├── README.md
├── requirements.txt
│
├── data/
│   ├── raw/                          # Raw bibliographic data (Scopus and Web of Science)
│   │   ├── scopus_base.csv
│   │   └── wos_base.xls
│   │
│   ├── processed/                    # Intermediate datasets after filtering and normalization
│   │   ├── filtereds_merged.xlsx
│   │   ├── merged_exported_scopus_mapped.xlsx
│   │   ├── scopus_filtered.csv
│   │   ├── wos_filtered.csv
│   │   └── selection.csv
│   │
│   ├── ready/                        # Final dataset used in the scientometric analysis
│   │   └── final_dataset_merged.csv
│   │
│   └── controlled_vocabulary/        # Controlled vocabulary for keyword normalization
│       ├── tesauro.csv               # Thesaurus for term equivalence and normalization
│       └── words_remove.txt          # Domain-specific stoplist for keyword filtering
│
└── src/
    ├── bibliometrix_analysis.R        # Bibliometric and thematic analysis (Bibliometrix/Biblioshiny)
    │
    └── notebooks/
        └── tratamento_artigos.ipynb  # Data preprocessing

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🔧 Tools and Technologies

The study combines Python-based data processing with specialized bibliometric tools:

• Python (pandas, numpy, matplotlib, seaborn)
• Bibliometrix / Biblioshiny (R) – descriptive indicators and thematic maps
• VOSviewer – co-authorship, co-citation, and keyword networks
• PyBIBix – bibliographic data parsing

⚠️ Some visualizations (e.g., VOSviewer maps) are generated externally and imported into this repository.

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▶️ How to Reproduce the Analysis

1. Clone the repository

git clone https://github.com/vic37get/scientometric_qnlp.git
cd scientometric_qnlp

2. Install dependencies

pip install -r requirements.txt

3. Add bibliographic data Place Scopus and Web of Science exports (.bib, .csv, or .txt) into:

data/raw/

4. Run preprocessing and analysis notebook Execute the notebooks:

• notebooks/tratamento_artigos.ipynb

5. (Optional) Run Biblioshiny

biblioshiny()

Then upload the processed datasets from data/processed/.

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📊 Main Outputs

The pipeline produces:

• Annual publication growth curves
• Lotka’s Law author productivity analysis
• Bradford’s Law core journals
• Country-level SCP vs MCP analysis
• Keyword co-occurrence networks
• Thematic evolution and thematic maps
• Application-focused keyword networks (after concept pruning)

These outputs directly support the figures and tables presented in the paper.

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📎 Citation

If you use this repository or build upon this work, please cite the article:

@ARTICLE{11271215,
  author={Silva, Victor R. and Barbosa, Fábio R. and Silva, Jasson C. and Santos, Francisco J. and Rabelo, Ricardo A. L. and Rodrigues, Joel J. P. C.},
  journal={IEEE Access}, 
  title={A Scientometric Review of Practical Applications in Quantum Natural Language Processing (QNLP): Trends, Gaps, and Research Opportunities}, 
  year={2025},
  volume={13},
  number={},
  pages={210169-210184},
  keywords={Natural language processing;Quantum computing;Bibliometrics;Computational modeling;Biological system modeling;Medical services;Databases;Market research;Data visualization;Training;Bibliometrics;natural language processing;quantum natural language processing;quantum computing;scientometrics},
  doi={10.1109/ACCESS.2025.3638646}}

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📄 License

This repository is released under the Creative Commons Attribution 4.0 (CC BY 4.0) license, consistent with the IEEE Access publication.

You are free to:

• Share and adapt the material
• Use it for academic and commercial purposes

As long as proper attribution is given.

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👤 Contact

Victor R. Silva Federal University of Piauí (UFPI) 📧 victor.silva@ufpi.edu.br

For questions, suggestions, or collaborations, feel free to open an issue or get in touch directly.

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