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

#<23> <20>L<EFBFBD>i<EFBFBD>n<EFBFBD>k<EFBFBD>e<EFBFBD>d<EFBFBD>I<EFBFBD>n<EFBFBD>t<EFBFBD> <0A> <0A>#<23> <20>L<EFBFBD>i<EFBFBD>n<EFBFBD>k<EFBFBD>e<EFBFBD>d<EFBFBD>I<EFBFBD>n<EFBFBD>t<EFBFBD> <0A> <0A>