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Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn, Google+, GitHub, and More (Englisch) Taschenbuch – 8. Oktober 2013


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Mining the social web, again When we first published "Mining the Social Web," I thought it was one of the most important books I worked on that year. Now that we're publishing a second edition (which I didn't work on), I find that I agree with myself. With this new edition, "Mining the Social Web" is more important than ever. While we're seeing more and more cynicism about the value of data, and particularly "big data," that cynicism isn't shared by most people who actually work with data. Data has undoubtedly been overhyped and oversold, but the best way to arm yourself against the hype machine is to start working with data yourself, to find out what you can and can't learn. And there's no shortage of data around. Everything we do leaves a cloud of data behind it: Twitter, Facebook, Google+ -- to say nothing of the thousands of other social sites out there, such as Pinterest, Yelp, Foursquare, you name it. Google is doing a great job of mining your data for value. Why shouldn't you? There are few better ways to learn about mining social data than by starting with Twitter; Twitter is really a ready-made laboratory for the new data scientist. And this book is without a doubt the best and most thorough approach to mining Twitter data out there. But that's only a starting point. We hear a lot in the press about sentiment analysis and mining unstructured text data; this book shows you how to do it. If you need to mine the data in web pages or email archives, this book shows you how. And if you want to understand how to people collaborate on projects, "Mining the Social Web" is the only place I've seen that analyzes GitHub data. All of the examples in the book are available on Github. In addition to the example code, which is bundled into IPython notebooks, Matthew has provided a VirtualBox VM that installs Python, all the libraries you need to run the examples, the examples themselves, and an IPython server. Checking out the examples isr

Über den Autor und weitere Mitwirkende

Matthew Russell, Chief Technology Officer 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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Amazon.com: 55 Rezensionen
20 von 20 Kunden fanden die folgende Rezension hilfreich
Must have if interested in mining social media 10. Oktober 2013
Von Bernard Enjolras - Veröffentlicht auf Amazon.com
Format: Taschenbuch
The second edition of Mining the Social Web is not just an update of the previous edition (including Google+, GitHub, and Twitter API 1.1) but a new book. The book has been rethought in its entirety with a focus on pedagogy and practical use of the code. With the help of a virtual machine and IPython notebook (both made available by the author) it is possible to run the code without difficulty. The book includes a Twitter Cookbook section which is very useful if you want to mine Twitter. In my opinion this book is the best introduction to real-world programming in Python. It introduces many concepts and tools related to modern web-programming and data-mining. Additionally it gives you the tools and the code for querying social media APIs and analyzing your data in a meaningful way. Matthew Russell has realized a tour de force with the new edition of this book: introducing advanced programming concepts and tools in a pedagogic, accessible and practical way.
10 von 10 Kunden fanden die folgende Rezension hilfreich
Easy to follow, practical, and fun! 5. November 2013
Von Greg - Veröffentlicht auf Amazon.com
Format: Taschenbuch
This book is extremely practical and has great code samples. It's easy to follow and fun! If you're interested in mining Twitter data, there is an (large) chapter focused entirely on reproducible code snippets that use the Twitter API.
11 von 12 Kunden fanden die folgende Rezension hilfreich
Read this book if you love working with data! 22. Januar 2014
Von Carsten Jørgensen - Veröffentlicht auf Amazon.com
Format: Taschenbuch
Book review - Mining the Social Web, 2nd Edition by Matthew A. Russell, O'Reilly Media

Introduction
Last year I read an article in Nature about Paul Erdős’s on the occasion of his 100th birthday. Outside mathematical circles Erdős is most known for the so called Erdős number. There are several different definitions of the Erdős number but according to Wikipedia it is defines as the "'collaborative distance' between a person and mathematician Paul Erdős, as measured by authorship of mathematical papers". So if you co-authored a paper with Erdős your Erdős number is 1. Your number will be 2 if you co-authored a paper with an author who wrote a paper directly with Erdős and so forth. Analyzing Erdős numbers is an application of social network theory and ever since I read the article I wanted to learn more about data mining applied to modern social media platforms. When researching for a book on this topic I came across Mining the Social Web and the books very practical approach convinced me to that this was the book I wanted to read.

Virtual Machine experience
The book is accompanied with a Virtual Machine experience that sets new standards for interactions between technical programming books and the code samples provided by the book. In no time you are up and running with the code samples in a IPython notebook that also can be edited and used as basis for your own data mining experiments. I would really love to see this approach adopted by other programming books.

The reader is gently guided through a software setup of VirtualBox and Vagrant and once these two programs have been installed it is just a matter of writing "vagrant up" in a terminal window and all of the necessary software used throughout the book will be installed and running in a virtual machine accessible through a web browser. Setting up the virtual machine might sound complicated but it is really quite easy. I tested the procedure for on both Mac and Windows and had no troubles getting the environment up and running in less than half an hour. And the really cool thing is that you don't have to install and manage a lot of dependencies yourself as well as you can delete everything afterwards just by deleting the virtual machine. The whole setup process is both described in the book and on videos found on the book's Github pages.

Data mining
Some knowledge and experience with Python is required fully understand the code samples. If you have experience from other modern programming languages you should not have troubles understanding basic Python code. So the choice of Python cannot be considered as a barrier for reading the book.

I am amazed of how well Russell mixes deep and complex theoretical knowledge with a very practical hands-on approach in such a way that both theory and code samples becomes very understandable. Not only does the book cover data mining of popular social media platforms as Twitter, LinkedIn, and Facebook but it also includes material on platforms as Google+ and Github which are usually not discussed in data mining books. After you have extracted data from some social media platform you need tools to analyze and visualize the data. Mining the Social Web gives an introduction to tools like Natural Language Toolkit and the JavaScript visualization library D3 and provides enough information for one to get started with such tools. Being able to store the extracted data is also an important feature and you will find code examples of storing data from Twitter in the popular noSQL database MongoDB.

The books does not cover social network theory in general nor graph theory so if you are looking for a book with a theoretical approach then this book is not for you. However most chapters in the book ends with a list of additional resources that can be used for further research.

Conclusion
This book is the best computer book I have read in several years. Social networks and data mining is a hot topic and reading Mining the Social Web will not only provide you knowledge about data mining but also supply practical code examples. In addition the books is an easy read and quite funny!

Disclosure
I review for the O`Reilly Reader Review Program and I want to be transparent about my reviews so you should know that I received a free copy of this ebook in exchange of my review.
11 von 12 Kunden fanden die folgende Rezension hilfreich
New Standard in Technical Books 8. November 2013
Von Brendon Unland - Veröffentlicht auf Amazon.com
Format: Taschenbuch Verifizierter Kauf
I have purchased just about every book available on social media data mining/ analytics, including the first edition of this book. What Matthew Russell has done with this second edition is amazing. With the purchase of this book, you get a fully functional virtual machine (available via download on GitHub.) As updates are made to the code for the book, you can easily pull them from GitHub. This eliminates the countless hours you spend downloading, configuring, troubleshooting, wondering if you got the right version of the needed software, etc. Within minutes you can read the book and type the code samples. Actually, the code is already there, you simply enter in some key values and watch the code run. You can then morph the code and see the effects of your changes.

Mining the Social Web is exceptionally well written covering all major social media platforms. Mr. Russell is also very approachable and answers questions very quickly.

I really can't say enough good things about this book and how it sets the bar high for future technical books!
7 von 7 Kunden fanden die folgende Rezension hilfreich
Excellent toolkit for Social Data Mining 3. November 2013
Von Mark Meanwell - Veröffentlicht auf Amazon.com
Format: Taschenbuch
Great guidebook to acquiring and analyzing data from leading social media sites, including Twittter, Facebook, Google +, LinkedIn and GitHub along with other web tips and tricks. The iPython notebook approach provides turn key like method to run examples and check results in line, which accelerates and reinforces the topics.

Whether you are new to social media API's and want a straightforward way to ramp up learning and discovery of social mining techniques or more seasoned user, this book has it covered. Chapter formats and exercises make it easy to work a variety of topics and are laid out in easy to follow and execute fashion.

Highly recommend, so get the book and get started!
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