Credits
Original Scraper by Danny Chrastil (@DisK0nn3cT): https://github.com/DisK0nn3cT/linkedin-gatherer
Modified by @vysecurity
Requirements
pip install beautifulsoup4
pip install thready
Change Log
[v0.1 BETA 12-07-2017] Additions:
- UI Updates
- Constrain to company filters
- Addition of Hunter for e-mail prediction
To-Do List
- 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.
Usage
Put in LinkedIn credentials in LinkedInt.py Put Hunter.io API key in LinkedInt.py Run LinkedInt.py and follow instructions
Example
██╗ ██╗███╗ ██╗██╗ ██╗███████╗██████╗ ██╗███╗ ██╗████████╗
██║ ██║████╗ ██║██║ ██╔╝██╔════╝██╔══██╗██║████╗ ██║╚══██╔══╝
██║ ██║██╔██╗ ██║█████╔╝ █████╗ ██║ ██║██║██╔██╗ ██║ ██║
██║ ██║██║╚██╗██║██╔═██╗ ██╔══╝ ██║ ██║██║██║╚██╗██║ ██║
███████╗██║██║ ╚████║██║ ██╗███████╗██████╔╝██║██║ ╚████║ ██║
╚══════╝╚═╝╚═╝ ╚═══╝╚═╝ ╚═╝╚══════╝╚═════╝ ╚═╝╚═╝ ╚═══╝ ╚═╝
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