Data mining Twitter using Python and the Repustate Social API.

gistfile1.py

import json
import requests

API_KEY = 'YOUR_API_KEY'
BASE_URL = 'http://social.repustate.com/%(api_key)s/%(call)s.json'

# Create a new data source.
kwargs = {'api_key':API_KEY, 'call':'add-datasource'}
response = requests.post(BASE_URL % kwargs, {'name':'Testing', 'language':'en', 'niche':'general'})
datasource_id = json.loads(response.content)['datasource_id']

# Add a monitoring rule to our newly created data source.
kwargs['call'] = 'add-rule'
post_data = { 
    'datasource_id':datasource_id,
    'source_type':'twitter-all',
    'query':'iOS7',
    'min_followers':1000,
    'exclude_retweets':1
}
response = requests.post(BASE_URL % kwargs, post_data)

# Assuming a little bit of time has passed and Repustate has collected data, we
# can now create a visualization.

# Let's generate a graph showing the breakdown of male vs. female for positive sentiment.
kwargs['call'] = 'visualize'
post_data = { 
    'datasource_id':datasource_id,
    'since':'2013-06-01',
    'until':'2014-01-01',
    'filter_type':'gender',
    'sentiment':'positive',
}
response = requests.post(BASE_URL % kwargs, post_data)
url = json.loads(response.content)['url']

# Here's our URL, ready to download.
print url