By Matthew A. Russell
Millions of public Twitter streams harbor a wealth of knowledge, and when you mine them, you could achieve a few useful insights. This brief and concise e-book deals a suite of recipes that can assist you extract nuggets of Twitter info utilizing easy-to-learn Python instruments. every one recipe bargains 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 info
* Create and examine graphs of retweet relationships
* Use the streaming API to reap tweets in realtime
* Harvest and learn pals and fans
* observe friendship cliques
* Summarize webpages from brief URLs
This booklet is an ideal better half to O’Reilly's Mining the Social Web.
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Extra info for 21 Recipes for Mining Twitter
In the working example, all that needs to happen is a sum of the values, 3 + 3 + 3 +, ... + 3, in order to come to a final aggregate value. A discussion of rereduce is inherently a slightly advanced topic, but is fundamental to an understanding of the map/reduce paradigm. It may bend your brain just a little bit, but manually working through some examples is very conducive to getting the hang of it. Once the frequency maps are computed, the details for visualizing the entities in a tag cloud amount to little more than scaling the size of each entity and writing out the JSON data structure that the WP-Cumulus tag cloud expects.
Com/post/383276-anyone-knows-the-formula-for-font-s min_freq = raw_output max_freq = raw_output[-1] def weightTermByFreq(f): return (f - min_freq) * (MAX_FONT_SIZE - MIN_FONT_SIZE) / (max_freq - min_freq) + MIN_FONT_SIZE weighted_output = [[i, i, weightTermByFreq(i)] for i in raw_output] # Substitute the JSON data structure into the template. name # Open up the web page in your browser. name) 32 | The Recipes The following discussion is somewhat advanced and focuses on trying to explain how the summing_reducer function works, depending on whether the value of its rereduce parameter is True or False.
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. Example 1-23 illustrates how to use make_twitter_request in the manner described to fetch some friends and followers ids.
21 Recipes for Mining Twitter by Matthew A. Russell