Mining the Social Web
and over 900,000 other books are available for Amazon Kindle .
Learn more
| ||||||||||||||||||
 Trade In this Item for up to £7.35
Trade in Mining the Social Web: Analyzing Data from Facebook, Twitter, LinkedIn, and Other Social Media Sites for an Amazon.co.uk gift card of up to £7.35, which you can then spend on millions of items across the site. Trade-in values may vary (terms apply). Find more products eligible for trade-in.
|
Frequently Bought Together
Product details
|
More About the Authors
Discover books, learn about writers, and more.
Product Description
Book Description
Finding Needles in the Social Haystack
Product Description
Facebook, Twitter, and LinkedIn generate a tremendous amount of valuable social data, but how can you find out who's making connections with social media, what they’re talking about, or where they’re located? This concise and practical book shows you how to answer these questions and more. You'll learn how to combine social web data, analysis techniques, and visualization to help you find what you've been looking for in the social haystack, as well as useful information you didn't know existed.
Each standalone chapter introduces techniques for mining data in different areas of the social Web, including blogs and email. All you need to get started is a programming background and a willingness to learn basic Python tools.
- Get a straightforward synopsis of the social web landscape
- Use adaptable scripts on GitHub to harvest data from social network APIs such as Twitter, Facebook, and LinkedIn
- Learn how to employ easy-to-use Python tools to slice and dice the data you collect
- Explore social connections in microformats with the XHTML Friends Network
- Apply advanced mining techniques such as TF-IDF, cosine similarity, collocation analysis, document summarization, and clique detection
- Build interactive visualizations with web technologies based upon HTML5 and JavaScript toolkits
"Let Matthew Russell serve as your guide to working with social data sets old (email, blogs) and new (Twitter, LinkedIn, Facebook). Mining the Social Web is a natural successor to Programming Collective Intelligence: a practical, hands-on approach to hacking on data from the social Web with Python."
--Jeff Hammerbacher, Chief Scientist, Cloudera
"A rich, compact, useful, practical introduction to a galaxy of tools, techniques, and theories for exploring structured and unstructured data."
--Alex Martelli, Senior Staff Engineer, Google
Suggested Tags from Similar Products(What's this?)Be the first one to add a relevant tag (keyword that's strongly related to this product)
|
What Other Items Do Customers Buy After Viewing This Item?
Customer Reviews
|
Share your thoughts with other customers:
|
||||||||||||||||||||||
|
Most Helpful Customer Reviews
9 of 9 people found the following review helpful:
3.0 out of 5 stars
Interesting but lacking in practical examples,
By David Bowers "@iamdavebowers" (Boston, MA) - See all my reviews
This review is from: Mining the Social Web: Analyzing Data from Facebook, Twitter, LinkedIn, and Other Social Media Sites (Paperback)
Mining the Social Web by Matthew Russell, published by O'Reilly, is an overview of data mining popular websites such as Twitter, Facebook, Linkedin and more. It even goes as far as touching on the semantic web and the not-so-popular Google Buzz.Each area is covered with a brief explanation of how to set up any programs, a look at the API, some examples of the mining code and a couple of suggestions on how to use it. The book covers a lot of ground which is either a blessing, or a curse, depending on what you're looking for. I found this book only partially useful because it really didn't delve deeply into any one area. It could have focused solely on Twitter, showing more business, or personal specific examples. Instead we had sections on the semantic web and e-mail mining, while interesting, are not specific to a 'social web' book. Linkedin makes an appearance, one example used being grouping all your contacts with the same job title. How useful is this really? I understand it's an example but that's what I wanted more of - really useful examples I could apply to day-to-day business activities. I'd recommend this book to anybody with some programming knowledge (Python being a huge help) who wants to know a little about a lot. For those looking for in-depth social mining techniques, I'd look elsewhere. Disclosure: I received a free electronic copy of this book as party of the O'Reilly Blogger program
1 of 3 people found the following review helpful:
5.0 out of 5 stars
Exactly like it promises,
By
This review is from: Mining the Social Web: Analyzing Data from Facebook, Twitter, LinkedIn, and Other Social Media Sites (Paperback)
I found the book and thought "yes, this is what I've been looking for!". I read the book, and it delivers exactly what is promises. The handy code examples (in python) are freely available on the web.
Share your thoughts with other customers: Create your own review
Most Helpful Customer Reviews on Amazon.com (beta) Amazon.com:
4.5 out of 5 stars (15 customer reviews) 14 of 15 people found the following review helpful:
4.0 out of 5 stars
pure fun,
By H. Smith "profhal" - Published on Amazon.com
This review is from: Mining the Social Web: Analyzing Data from Facebook, Twitter, LinkedIn, and Other Social Media Sites (Paperback)
Mining the Social Web does a great job of introducing a wide variety of techniques and wealth of resources for exploring freely available social data and personal information. If you are willing to spend the time tinkering with the examples, the book is pure fun. It offers a nice compliment to Segaran's Programming Collective Intelligence: Building Smart Web 2.0 Applications. The two books overlap but where they do offer different perspectives and explanations of common techniques (e.g., TF-IDF, cosine similarity, Jaccard index). If you are well-versed in data mining the web you may find much of the discussion familiar. If you have only been casually engaged to date, your toolbox will fill quickly.In order to work with the book's examples related to LinkedIn and Facebook you really need to have a robust collection of connections. In terms of the source code itself, most of it worked as is. I wasn't able to install the Buzz library which limited my interaction with material in chapter 7 and opted to not get involved with the LinkedIn or Facebook but found the discussions around them easy to follow. By far my favorite chapter in the book was chapter 8, "Blogs et al.: Natural Language Processing (and Beyond)..." It was quite fascinating and caused my reading list to grow considerably. 9 of 10 people found the following review helpful:
5.0 out of 5 stars
A book that covers an awesome lot of ground,
By Ricardo Bánffy - Published on Amazon.com
This review is from: Mining the Social Web: Analyzing Data from Facebook, Twitter, LinkedIn, and Other Social Media Sites (Paperback)
This book covers a lot of ground. It's, at times, a bit vertiginous in the amount of subjects and technologies it touches per chapter, and is not always easy to follow. It can also introduce so many interesting things that, by the time you finished becoming familiar with all of them, after wandering for hours on the web, jumping from interesting technology to interesting technology, you may have forgotten what took you to these places and wonder where you were in the book. Time spent reading it is, however, time very well spent. When you finish it, you will have at least a cursory familiarity with tools like OAuth, CouchDB, Redis, MapReduce, NumPy (and the Python programming language, albeit it will help you a lot if you know your way around Python before you start the book), Graphviz, SIMILE widgets, NLTK, various service APIs and data formats, and will be well equipped to explore those rich datasets on your own. The chapters are well compartmentalized and it's easy to pick chapters to read according to your needs. I know that, when I face the problems they tackle, I will do exactly that.If you do any kind of analysis and visualization of social-generated data that's on the web, this book is a good pick. Even if your datasets are not from the web, you may find the parts on analysis and visualization very interesting. 3 of 3 people found the following review helpful:
5.0 out of 5 stars
Easy to read. I tore through it,
By Wiebe de Jong - Published on Amazon.com
This review is from: Mining the Social Web: Analyzing Data from Facebook, Twitter, LinkedIn, and Other Social Media Sites (Paperback)
Some basic programming ability is a must for this book, as the first page starts with installing the Python development tools. If you don't know Python, that is okay since all the code is easy to follow. Everything you need to develop and run the examples is described step by step with clear instructions at every point.Once you get comfortable with the basics, the author quickly moves from topic to topic, giving a good introduction into many aspects of how to mine data and generate useful conclusions. Some of the examples include accessing your twitter feed with OAuth, processing feeds to determine influence, using set-wise opeations with redis to determine which of your friends are also followers, storing data in CouchDB, using map-reduce to determine the most popular mentions and topics, natural language processing, and seeing data with various visualization tools. And that was just for Twitter. The book continues on with examples of processing mailboxes, LinkedIn, Google Buzz, blogs, Facebook, and the Semantic Web. The examples show how easy it is to gather and analyze data from all these social web sites. With a good breadth of coverage, I highly recommend this book for anyone wanting to learn to process and visualize large amounts of data, either from the social web or any other data source. |
|
Listmania!
Look for similar items by category
Look for similar items by subject
Feedback
- Would you like to update product info or give feedback on images?
- I am the Author, and I want to comment on my book.
- I am the Publisher, and I want to comment on this book.
|
