Download 21 Recipes for Mining Twitter by Matthew A. Russell PDF

By Matthew A. Russell

Millions of public Twitter streams harbor a wealth of information, and when you mine them, you could achieve a few important insights. This brief and concise publication bargains a set of recipes that can assist you extract nuggets of Twitter details utilizing easy-to-learn Python instruments. every one recipe deals a dialogue of ways and why the answer works, so that you can quick adapt it to suit your specific wishes. The recipes contain strategies to:
* Use OAuth to entry Twitter information
* Create and examine graphs of retweet relationships
* Use the streaming API to reap tweets in realtime
* Harvest and study acquaintances and fans
* notice friendship cliques
* Summarize webpages from brief URLs

This ebook is an ideal spouse to O’Reilly's Mining the Social Web.

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Difference(s2) set([1, 3]) Given that you’ll more than likely get to the point of analyzing fairly large amounts of friends and followers with setwise operations, you might find yourself pulling data from a database such as SQLite, performing a set operation in memory with Python, and then storing the result back to your database. While there’s nothing necessarily wrong with this approach, you should at least be aware of Redis, a project that makes this type of computation much more efficient. You might think of Redis as a “data structures server”—it’s a key-value store, but the values for each key can be typed.

It’s not terribly uncommon for you to want to fetch all of the friends or followers for a group of users, and for the more popular Twitterers, it’ll take more than your 350 requests per-hour rate limit will afford you. 75 million ids per hour. 75 million followers. Lady Gaga is one of the most (if not the most) popular, with around 8 million followers. 5 hours, which is a bit of a wait, but it’s still pretty amazing that you could harvest that much data in less than a business day. html) can be used to layer on some additional sugar to illustrate how you could ultimately create the get_friends_ids and get_followers_ids functions that you’ve really always wanted.

Its document-oriented nature is designed so that it can natively store JSON data (the response format from Twitter’s API) and its builtin map/reduce functionality provides a great fabric for many types of tweet analysis that you’ll encounter. ini configuration file. ) The effect of this change is that you can install additional query servers for languages other than JavaScript (which is the default). 6/bin/couchpy With CouchDB installed and running on your local machine, the couchdb package installed, and your configuration updated to use couchpy as a Python query server, you’re all set to use the script presented in Example 1-20 to harvest timeline data and persist it in CouchDB.

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