21 Recipes for Mining Twitter
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21 Recipes for Mining Twitter

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by Matthew A. Russell
     
 

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Millions of public Twitter streams harbor a wealth of data, and once you mine them, you can gain some valuable insights. This short and concise book offers a collection of recipes to help you extract nuggets of Twitter information using easy-to-learn Python tools. Each recipe offers a discussion of how and why the solution works, so you can quickly adapt it to fit

Overview

Millions of public Twitter streams harbor a wealth of data, and once you mine them, you can gain some valuable insights. This short and concise book offers a collection of recipes to help you extract nuggets of Twitter information using easy-to-learn Python tools. Each recipe offers a discussion of how and why the solution works, so you can quickly adapt it to fit your particular needs. The recipes include techniques to:

  • Use OAuth to access Twitter data
  • Create and analyze graphs of retweet relationships
  • Use the streaming API to harvest tweets in realtime
  • Harvest and analyze friends and followers
  • Discover friendship cliques
  • Summarize webpages from short URLs

This book is a perfect companion to O’Reilly's Mining the Social Web.

Product Details

ISBN-13:
9781449303167
Publisher:
O'Reilly Media, Incorporated
Publication date:
03/07/2011
Pages:
72
Product dimensions:
6.90(w) x 9.00(h) x 0.30(d)

Meet the Author

Matthew Russell, Vice President of Engineering at Digital Reasoning Systems (http://www.digitalreasoning.com/) and Principal at Zaffra (http://zaffra.com), is a computer scientist who is passionate about data mining, open source, and web application technologies. He’s also the author of Dojo: The Definitive Guide (O’Reilly).

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21 Recipes for Mining Twitter 4 out of 5 based on 0 ratings. 1 reviews.
rbanffy More than 1 year ago
This is a very short, very practical way to get you started exploring the Twitter APIs on your own. It offers a decent amount of code no experienced developer should have much trouble understanding and applying to his or her own needs. The cost per page is not exactly attractive and some readers may want a more in-depth less cookbook-like experience. If you are in a hurry to extract data from Twitter, this book may be for you - for less than $20 for the electronic edition, it will spare you more than that in time spent figuring out libraries and APIs. Plus, it offers some intro on many other interesting libraries that can be applied to a lot of problems besides Twitter mining.