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 ebook bargains a set of recipes that will help you extract nuggets of Twitter details utilizing easy-to-learn Python instruments. each one recipe bargains a dialogue of ways and why the answer works, so that you can speedy adapt it to suit your specific wishes. The recipes comprise strategies to:
* Use OAuth to entry Twitter facts
* Create and examine graphs of retweet relationships
* Use the streaming API to reap tweets in realtime
* Harvest and research pals and fans
* realize 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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Extra info for 21 Recipes for Mining Twitter
When Q1 becomes empty, it means that all of these nodes have been visited, and the process repeats itself for the nodes in Q2, with Q1 now being used to keep track of neighbors. Once a suitable depth has been reached, the traversal terminates. A breadth-first traversal is easy to implement, and the neighbors for each node can be stored on disk and later analyzed as a graph. The two characteristics that govern the space complexity of a breadth-first traversal are the depth of the traversal and the average branching factor of each node in the graph.
Example 1-23. argv) if __name__ == '__main__': # Not authenticating lowers your rate limit to 150 requests per hr. # Authenticate to get 350 requests per hour. ids) # Ditto if you want to do the same thing to get followers... = 0: # Use make_twitter_request via the partially bound callable... stderr, 'Fetched %i total ids for %s' % (len(ids), SCREEN_NAME) # Consider storing the ids to disk during each iteration to provide an # an additional layer of protection from exceptional circumstances.
Luhn determined that it is often the case that sentences containing frequently appearing terms are the most important sentences, and the more closely together the frequently appearing terms occur, the better. Example 1-22 illustrates a routine for fetching a web page, extracting its text, and using Luhn’s algorithm to summarize the text in the web page. NLTK is used to segment the sentences into text, and the rest of the routine is fairly algorithmic. Luhn’s original paper is well worth a read and provides a very easy-to-understand discussion of this approach.