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