By Matthew A. Russell
Millions of public Twitter streams harbor a wealth of information, and when you mine them, you could achieve a few helpful insights. This brief and concise ebook bargains a suite of recipes that can assist you extract nuggets of Twitter details utilizing easy-to-learn Python instruments. each one recipe deals a dialogue of the way and why the answer works, so that you can speedy adapt it to suit your specific wishes. The recipes comprise suggestions to:
* Use OAuth to entry Twitter information
* Create and learn graphs of retweet relationships
* Use the streaming API to reap tweets in realtime
* Harvest and examine neighbors and fans
* notice friendship cliques
* Summarize webpages from brief URLs
This e-book is an ideal spouse to O’Reilly's Mining the Social Web.
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Extra info for 21 Recipes for Mining Twitter
If you choose to take this approach, be sure to take advantage of the since_id keyword parameter to request only tweets that have been updated since you last checked. 9 Making Robust Twitter Requests Problem You want to write a long-running script that harvests large amounts of data, such as the friend and follower ids for a very popular Twitterer; however, the Twitter API is 22 | The Recipes inherently unreliable and imposes rate limits that require you to always expect the unexpected. Solution Write an abstraction for making twitter requests that accounts for rate limiting and other types of HTTP errors so that you can focus on the problem at hand and not worry about HTTP errors or rate limits, which are just a very specific kind of HTTP error.
Argv KW['screen_name'] = USER if TIMELINE_NAME == 'home' and MAX_PAGES > 4: MAX_PAGES = 4 if TIMELINE_NAME == 'user' and MAX_PAGES > 16: MAX_PAGES = 16 if TIMELINE_NAME == 'public': MAX_PAGES = 1 # Authentication is needed for harvesting home timelines. # Don't forget to add keyword parameters to the oauth_login call below # if you don't have a token file on disk. t = oauth_login() # Establish a connection to a CouchDB database. PreconditionFailed, e: # Already exists, so append to it, keeping in mind that duplicates could occur.
Db = server[DB] # # # # Try to avoid appending duplicate data into the system by only retrieving tweets newer than the ones already in the system. A trivial mapper/reducer combination allows us to pull out the max tweet id which guards against duplicates for the home and user timelines. It has no effect for the public timeline. # For each tweet, emit tuples that can be passed into a reducer to find the maximum # tweet value. def id_mapper(doc): yield (None, doc['id']) # Find the maximum tweet id.
21 Recipes for Mining Twitter by Matthew A. Russell