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Product Description
Book Description
Distilling Rich Information from Messy Data
Product Description
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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Customer Reviews
Most Helpful Customer Reviews
1 of 1 people found the following review helpful
Format:Paperback
There's a lot of very useful information on Twitter. Not only information related to your area of expertise and a window into what your customers need and want from your products but also important details about who your potential customers are as well as trends, patterns, and cycles that may directly or indirectly affect your business.
There are two main issues when using Twitter for business: volume and organization. Essentially, how can you quickly and efficiently sift through thousands of tweets each day to locate those hidden gems and then how can you best organize this raw data to gleam insight that could help your business performance.
There are a growing number of Twitter apps out there. A lot are still focusing on the searching and collecting of particular topics, mostly for social usage. 21 Recipes for Mining Twitter focuses on apps that would be useful for business purposes: mining that data and analyzing it. As the title suggests, it is a recipe book with potential Twitter data uses and the basic code to solve the problem. Assuming you're familiar with programming, you can then play with and manipulate the code to better suit your specific needs. Invaluable custom apps, or at least a good solid start in doing so.
There are two main issues when using Twitter for business: volume and organization. Essentially, how can you quickly and efficiently sift through thousands of tweets each day to locate those hidden gems and then how can you best organize this raw data to gleam insight that could help your business performance.
There are a growing number of Twitter apps out there. A lot are still focusing on the searching and collecting of particular topics, mostly for social usage. 21 Recipes for Mining Twitter focuses on apps that would be useful for business purposes: mining that data and analyzing it. As the title suggests, it is a recipe book with potential Twitter data uses and the basic code to solve the problem. Assuming you're familiar with programming, you can then play with and manipulate the code to better suit your specific needs. Invaluable custom apps, or at least a good solid start in doing so.
Most Helpful Customer Reviews on Amazon.com (beta)
Amazon.com:
7 reviews
2 of 2 people found the following review helpful
Matthew Russell's 21 Recipes for Mining Twitter
25 April 2011
Format:Paperback
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.
4 of 6 people found the following review helpful
21 Recipes for Mining Twitter by Matthew Russell
14 Mar 2011
Format:Paperback
"21 Recipes for Mining Twitter" by Matthew Russell provides readers with a problem-oriented crash course in using Python and freely available third party Python packages to mine social data from Twitter. It assumes familiarity with Python and makes quick progress through extracting and using different types of user and streaming data available from the Twitter API via the twitter package in particular.
I like the general approach of calling out problems to be solved, then addressing them one by one with a "recipe" for each. Some might complain that this approach results in disjointedness from one recipe to the next, but in fact that's a feature, not a bug. "21 Recipes for Mining Twitter" is actually a spin-off of Russell's more in depth "Mining the Social Web: Analyzing Data from Facebook, Twitter, LinkedIn, and Other Social Media Sites" (also an O'Reilly book). The latter shows many more examples, and not just for mining Twitter, but also for harvesting and analyzing social data from many other services and APIs as well. So if you need down-and-dirty recipes for Twitter alone, get this book, and if you need more blanks filled in for Twitter mining and/or information on accessing data from other social services, get the other book. Or heck, get them both!
This book is recommended for: Anyone already familiar with programming who is looking to solve specific problems using Twitter data. It's a slam dunk for Python programmers.
Recommended with reservations for: Non-programmers interested in social data analysis, with the caveat that they will probably need to spend some time working through the Python.org tutorial or getting up to speed with Python elsewhere before they can make great progress with this book.
Disclaimer: I know the author and have worked with him in various capacities on other projects, but not this book. Even if I didn't know him, however, I'd still love and make use of this book.
I like the general approach of calling out problems to be solved, then addressing them one by one with a "recipe" for each. Some might complain that this approach results in disjointedness from one recipe to the next, but in fact that's a feature, not a bug. "21 Recipes for Mining Twitter" is actually a spin-off of Russell's more in depth "Mining the Social Web: Analyzing Data from Facebook, Twitter, LinkedIn, and Other Social Media Sites" (also an O'Reilly book). The latter shows many more examples, and not just for mining Twitter, but also for harvesting and analyzing social data from many other services and APIs as well. So if you need down-and-dirty recipes for Twitter alone, get this book, and if you need more blanks filled in for Twitter mining and/or information on accessing data from other social services, get the other book. Or heck, get them both!
This book is recommended for: Anyone already familiar with programming who is looking to solve specific problems using Twitter data. It's a slam dunk for Python programmers.
Recommended with reservations for: Non-programmers interested in social data analysis, with the caveat that they will probably need to spend some time working through the Python.org tutorial or getting up to speed with Python elsewhere before they can make great progress with this book.
Disclaimer: I know the author and have worked with him in various capacities on other projects, but not this book. Even if I didn't know him, however, I'd still love and make use of this book.
Decent
4 Sep 2011
Format:Paperback|Amazon Verified Purchase
There is no problem with the book, just a little tough for those new to Python. I would recommend this for those who know Python, but encourage those who don't to be prepared for a couple hours.
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