By Matthew A. Russell
Millions of public Twitter streams harbor a wealth of knowledge, and when you mine them, you could achieve a few precious insights. This brief and concise booklet deals a suite of recipes that can assist you extract nuggets of Twitter info utilizing easy-to-learn Python instruments. every one recipe deals a dialogue of the way and why the answer works, so that you can fast adapt it to suit your specific wishes. The recipes contain ideas to:
* Use OAuth to entry Twitter facts
* Create and research graphs of retweet relationships
* Use the streaming API to reap tweets in realtime
* Harvest and study buddies and fans
* observe friendship cliques
* Summarize webpages from brief URLs
This publication is an ideal significant other to O’Reilly's Mining the Social Web.
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Additional info for 21 Recipes for Mining Twitter
Discussion A set is an unordered collection of items, and basic setwise operations such as difference and intersection can answer many interesting questions you might have. 14 Performing Setwise Operations on Friendship Data | 39 common for all of those users by computing the intersection of all of the sets of follower ids. Likewise, the intersection of a particular user’s friend ids and follower ids could be interpreted as the user’s “mutual friends”—the friends that are following back. Computing who isn’t following you back is also just a setwise operation away.
Redis() # Not authenticating lowers your rate limit to 150 requests per hr. # Authenticate to get 350 requests per hour. com', api_version='1') # Harvest some friend ids. = 0: # Use make_twitter_request via the partially bound callable... response = get_friends_ids(screen_name=SCREEN_NAME, cursor=cursor) # Add the ids to the set in redis with the sadd (set add) operator. scard(rid) >= MAX_IDS: break # Harvest some follower ids. = 0: # Use make_twitter_request via the partially bound callable...
21 Recipes for Mining Twitter by Matthew A. Russell