Original Scraper by Danny Chrastil (@DisK0nn3cT): https://github.com/DisK0nn3cT/linkedin-gatherer
Modified by @vysecurity
pip install beautifulsoup4 pip install thready [v0.1 BETA 12-07-2017] Additions:
- UI Updates
- Constrain to company filters
- Addition of Hunter for e-mail prediction
- Allow for horizontal scraping and mass automated company domain, and format prediction per company
- Add Natural Language Processing techniques on titles to discover groups of similar titles to be stuck into same "department". This should then be visualised in a graph.
Put in LinkedIn credentials in LinkedInt.py Put Hunter.io API key in LinkedInt.py Run LinkedInt.py and follow instructions
██╗ ██╗███╗ ██╗██╗ ██╗███████╗██████╗ ██╗███╗ ██╗████████╗ ██║ ██║████╗ ██║██║ ██╔╝██╔════╝██╔══██╗██║████╗ ██║╚══██╔══╝ ██║ ██║██╔██╗ ██║█████╔╝ █████╗ ██║ ██║██║██╔██╗ ██║ ██║ ██║ ██║██║╚██╗██║██╔═██╗ ██╔══╝ ██║ ██║██║██║╚██╗██║ ██║ ███████╗██║██║ ╚████║██║ ██╗███████╗██████╔╝██║██║ ╚████║ ██║ ╚══════╝╚═╝╚═╝ ╚═══╝╚═╝ ╚═╝╚══════╝╚═════╝ ╚═╝╚═╝ ╚═══╝ ╚═╝ Providing you with Linkedin Intelligence Author: Vincent Yiu (@vysec, @vysecurity) Original version by @DisK0nn3cT [*] Enter search Keywords (use quotes for more percise results) "General Motors" [*] Enter filename for output (exclude file extension) generalmotors [*] Filter by Company? (Y/N): Y [*] Specify a Company ID (Provide ID or leave blank to automate): [*] Enter e-mail domain suffix (eg. contoso.com): gm.com [*] Select a prefix for e-mail generation (auto,full,firstlast,firstmlast,flast,first.last,fmlast): auto [*] Automaticly using Hunter IO to determine best Prefix [!]{first}.{last} [+] Found first.last prefix