Matthew A Russell

21 Recipes for Mining Twitter

Distilling Rich Information from Messy Data

• Broschiertes Buch

Produktbeschreibung

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.

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Produktdetails

• Verlag: O'Reilly Media
• Seitenzahl: 72
• Erscheinungstermin: 5. April 2011
• Englisch
• Abmessung: 233mm x 174mm x 7mm
• Gewicht: 143g
• ISBN-13: 9781449303167
• ISBN-10: 1449303161
• Artikelnr.: 33117043

Autorenporträt

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).

Inhaltsangabe

Preface
Introduction
Conventions Used in This Book
Using Code Examples
Safari® Books Online
How to Contact Us
Chapter 1: The Recipes
1.1 Using OAuth to Access Twitter APIs
1.2 Looking Up the Trending Topics
1.3 Extracting Tweet Entities
1.4 Searching for Tweets
1.5 Extracting a Retweet's Origins
1.6 Creating a Graph of Retweet Relationships
1.7 Visualizing a Graph of Retweet Relationships
1.8 Capturing Tweets in Real-time with the Streaming API
1.9 Making Robust Twitter Requests
1.10 Harvesting Tweets
1.11 Creating a Tag Cloud from Tweet Entities
1.12 Summarizing Link Targets
1.13 Harvesting Friends and Followers
1.14 Performing Setwise Operations on Friendship Data
1.15 Resolving User Profile Information
1.16 Crawling Followers to Approximate Potential Influence
1.17 Analyzing Friendship Relationships such as Friends of Friends
1.18 Analyzing Friendship Cliques
1.19 Analyzing the Authors of Tweets that Appear in Search Results
1.20 Visualizing Geodata with a Dorling Cartogram
1.21 Geocoding Locations from Profiles (or Elsewhere)