Get 21 Recipes for Mining Twitter PDF

By Matthew A. Russell

ISBN-10: 1449303161

ISBN-13: 9781449303167

Millions of public Twitter streams harbor a wealth of knowledge, and when you mine them, you could achieve a few helpful insights. This brief and concise booklet deals a set of recipes that will help you extract nuggets of Twitter info utilizing easy-to-learn Python instruments. each 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 comprise suggestions to:
* Use OAuth to entry Twitter facts
* Create and study graphs of retweet relationships
* Use the streaming API to reap tweets in realtime
* Harvest and learn acquaintances and fans
* become aware of friendship cliques
* Summarize webpages from brief URLs

This publication is an ideal better half to O’Reilly's Mining the Social Web.

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Extra resources for 21 Recipes for Mining Twitter

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Id=214 for why you can't use the user id values. has_key(screen_name): screen_name_to_tweet_ids[screen_name] = [] screen_name_to_tweet_ids[screen_name] += [ result['id'] ] # Use the /users/lookup resource to resolve profile information for # these screen names. keys()) # Extract the home location for each user. 19 Analyzing the Authors of Tweets that Appear in Search Results | 53 # "United States" or something else that won't geocode to a specific coordinate # on a map. items()]) # Use the various screen_name_to{tweet_ids, info, location} maps to determine # interesting things about the people who appear in the search results.

Which represents an intermediate result. When this happens, it’s necessary for the output of these reductions to be rereduced, in which case the rereduce flag will be set to True. The value for the keys is of no consequence, since we are already operating on output that’s guaranteed to have been produced from the same keys. 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.

Hashes, lists, sets, and ordered sets are just a few of the possibilities. Redis is trivial to get up and running, written in extremely lean C code, and can perform operations in memory and store the result as another key-value pair. In the case of sets, that means that you can perform intersections and differences directly in Redis and fetch the result in a manner that is dramatically more efficient than fetching data, performing the computation in your own application logic, and then sending data back to whatever you retrieved it for storage.

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21 Recipes for Mining Twitter by Matthew A. Russell

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