Determine a persons interests based on who they follow

twit_interests.py

#!/usr/bin/env python
"""Twit Interest will infer a persons interests based upon the most common words in the descriptions of those they follow"""
from twython import Twython
from collections import Counter
import sys

#Twython isn't a standard module.  "python -m pip install twython" to install it.  If no pip run this https://bootstrap.pypa.io/get-pip.py

twit = Twython(<your twitter APP KEY HERE> , < Your twitter Secret key here >)
#Need a key?  Go  https://apps.twitter.com/app/new   Create app. Put anything you want for the values in the app.
#After app is created get the API Key and SECRET from the "KEYS AND ACCESS TOKENS" tab.
#WARNING: Twitter limits you to around 1500 queries a day. This tool does one request per follower and looks at all their profiles.

if len(sys.argv) != 2:
    print('Usage - Pass one argument.  The twitter profile name. Do not include the @.\n Example: "python twit_interests.py cslewisdaily"')
    sys.exit(1)

c = Counter()

ignore_words = ['AND', "WE", "THAT","THAN","THEM", 'OF', "GET", "SO", "SOME",'THE', 'TO', 'FOR', 'MY', 'IN', 'IS', 'ARE', 'I', 'AT','ON', 'WITH', 'BY', 'NOT', 'THAT','TWEETS', 'WE', 'ALL', 'FROM', 'OWN', 'ABOUT', 'DO', 'YOU', 'OUR', 'THIS', 'THINGS', 'TEAM', 'YOUR', 'VIEWS',  'AN', 'TWITTER', 'THOUGHTS', 'MOST', 'NOW', 'OPEN', 'NEW', 'THERE', 'AS',  'WHO', 'ONE', 'OR', 'ALWAYS', 'IF', 'ME', 'THOSE', 'BE', 'AM', 'AROUND', 'LIKE', 'NO',  'SEE', 'GROUP', 'EXPRESSED',  'JUST', 'EVERY', 'BUT', 'MORE', "WHY", "BECAUSE", "SOLELY", "WITHIN", "SINCE", "ITS", "LOT", "OTHER","BOTH","UP","HERE","BETTER"]

ignore_chars = """.,'!-/:?"'#@"""


following = twit.get_friends_ids(screen_name=sys.argv[1]).get("ids")
num = len(following)
print("Analyzing {} accounts...\n".format(num))
for count, eachuser in enumerate(following):
    if count %10 == 0:
        print("\r|{0:-<50}| {1:3.2f}%".format("X"*( 50 * count//num), 100*count/num ),end="")
    description = twit.show_user(id=eachuser).get("description")
    description = description.translate(str.maketrans("", "", ignore_chars))
    description = [x for x in description.upper().split() if x not in ignore_words and len(x)>1]
    c.update(description)

print(c.most_common(1000))