linkedin-jobs-scraper-analysis
Using py-linkedin-jobs-scraper to extract job postings. Includes features for filtering job queries, collecting job data, and exporting results to CSV for further analysis. Ideal for job market research and data-driven insights.
Science Score: 44.0%
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○Scientific vocabulary similarity
Low similarity (10.8%) to scientific vocabulary
Repository
Using py-linkedin-jobs-scraper to extract job postings. Includes features for filtering job queries, collecting job data, and exporting results to CSV for further analysis. Ideal for job market research and data-driven insights.
Basic Info
- Host: GitHub
- Owner: m5991
- License: mit
- Language: Jupyter Notebook
- Default Branch: main
- Size: 2.49 MB
Statistics
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
# LinkedIn Job Scraper
A tool to scrape job listings from LinkedIn using Selenium and the open-source linkedin-jobs-scraper library. You can configure search queries, apply filters, and export results to CSV for analysis.
## Features
- Authenticate with LinkedIn using your LI_AT_COOKIE
- Define custom search queries with keywords, locations, filters, and limits
- Event-driven scraping: capture job data, performance metrics, and errors
- Export scraped job listings to linkedin_jobs.csv
## Installation
1. Clone the repository:
bash
git clone https://github.com/yourusername/linkedin-job-scraper.git
cd linkedin-job-scraper
2. Create and activate a virtual environment:
bash
python3 -m venv venv
source venv/bin/activate
3. Install Python dependencies:
bash
pip install linkedin-jobs-scraper python-dotenv selenium pandas jupyter
4. Create a .env file in the project root with your LinkedIn authentication cookie:
LI_AT_COOKIE=your_li_at_cookie_value
5. Ensure chromedriver and chrome are installed and available on your PATH or update paths in the notebook.
## Usage
1. Launch Jupyter:
bash
jupyter notebook src/linkedin.ipynb
2. Execute each cell in order. Scraped job listings will be saved to linkedin_jobs.csv.
## Contributing Please see CONTRIBUTING.md for details on how to contribute.
## Citation If you use this project in your research, please cite it as described in CITATION.md.
## License This project is licensed under the MIT License. See LICENSE for details.
Owner
- Name: Manuel Gastelum
- Login: m5991
- Kind: user
- Website: www.linkedin.com/in/ManuelGastelum/
- Repositories: 9
- Profile: https://github.com/m5991
Data Science Master's Student. Chemical Engineer
Citation (CITATION.md)
# Citation Information
If you use the LinkedIn Job Scraper in your research or projects, please cite it as follows:
**BibTeX**:
```bibtex
@misc{linkedin-job-scraper-analysis,
author = Manuel Gastelum,
title = {LinkedIn Job Scraper Analysis},
year = {2025},
howpublished = {\\url{https://github.com/m5991/linkedin-jobs-scraper-analysis}},
}
```
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