- Taschenbuch: 444 Seiten
- Verlag: O'Reilly and Associates; Auflage: 2 (8. Oktober 2013)
- Sprache: Englisch
- ISBN-10: 1449367615
- ISBN-13: 978-1449367619
- Größe und/oder Gewicht: 17,8 x 2,4 x 23,3 cm
- Durchschnittliche Kundenbewertung: Schreiben Sie die erste Bewertung
- Amazon Bestseller-Rang: Nr. 34.901 in Fremdsprachige Bücher (Siehe Top 100 in Fremdsprachige Bücher)
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, Principal at Zaffra, and author of several books on technology including Mining the Social Web (O'Reilly, 2013), now in its second edition. He is passionate about open source software development, data mining, and creating technology to amplify human intelligence. Matthew studied computer science and jumped out of airplanes at the United States Air Force Academy. When not solving hard problems, he enjoys practicing Bikram Hot Yoga, CrossFitting and participating in triathlons.
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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!
The reason this is such an important book is that it teaches non-experts to build simple systems for making decisions on data that is constantly up-to-date. It's an end-to-end manual for continuously gathering data (e.g. Twitter API), analyzing data (e.g. Natural Language Processing), and presenting information (e.g. D3). By significantly reducing the barrier to building these systems, Matthew has increased the number of people on the planet that can provide data for making proper decisions . . . and data always beats opinions.
This is one of the rare books that does a great job of introducing deep technical topics AND providing an easy, practical implementation. Unlike a lot of tech books, MtSW makes it trivial to get started through a combination of Vagrant, VirtualBox, IPython Notebook, and GitHub such that you can have all the updated examples up and running within minutes. I'm much more of a practitioner (read: Hacker) than a computer scientist so this is exactly the right amount of technical detail to try out an idea. As an example of technical depth, the coverage of the Twitter API is exactly the proper amount of detail to understand how to pull out tweets and start using the data right away, without slogging through the parts of the API that you'll never need. Better yet, the examples in the book are implemented in IPython, so you can start using it right away and tweaking the code so you can learn it interactively.
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!