Customer Reviews

Mining the Social Web: Analyzing Data from Facebook, Twitter, LinkedIn, and Other Social Media Sites

5 star:		(9)
4 star:		(5)
3 star:		(1)
2 star:		(0)
1 star:		(0)

 

 

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.

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.

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.

A good primer on capturing and visualizing social data, and tools to do more with what you find. Not for the faint of heart.
The book's title may be deceptive outside the worlds of web design or data analysis. A prerequisite knowledge of getting around in Python is a must from the start. The examples for each recipe are straightforward enough to be understood in most cases without explanation.
I liked the fast and furious look at each of the major social network platforms (plus emerging HTML5 Microformats and raw email headers), and while we get a great sense of how to cast our data nets, I ended up wanting more about evolving APIs. The text also details how structured or free each service warehouses, and is a great starting point for each lexical tool. The book is also conspicuously designed to be read online, and is extensively hyperlinked.
We can admire word frequency and visualization as to "what" text is important at a given time among your data pool. This should not be mistaken for "why" it's important, which is up to Semantic Web processing touched on but ultimately beyond the scope of this book. Interpretation is up to the reader.

Analyzing Data from Facebook, Twitter, LinkedIn, and Other Social Media Sites.
Mining the Social Web (by Matthew A. Russell) is very aptly named. The reader will get their hands dirty "mining" the Social Web. If you are not comfortable writing or reading Python script than this probably is not be the book for you. To get the most out of this book you will need to install and run Python on your computer (be it a Mac, Window or Linux based machine). If you have not been scared off by that, then you are in for some fun manipulating (really just reporting on) Social Media site data.

The Author does a very good job of outlining the prerequisites for the book, going though the process of installing Python and even a little troubleshooting to get it working properly. The Author takes this same care throughout the book, making few assumptions about the skill level of the reader, but also keeping it interesting for the more advanced readers as well. One unexpected example was mining your own mailbox, the original Social Media. The Mailbox examples are pretty extensive, if you make it through those then you should have little trouble with the remaining examples, which attack Facebook, Twitter, LinkedIn and Google Buzz. The book's examples can induce a certain amount of narcissism, but thats the heart to social media and not intended as a criticism of the book. However there are enough outward looking examples to stop you from becoming to self absorbed. As well the graphing examples provide some eye candy for the reader.

In closing, the book gives the reader a good understanding of Social Media data and what can be done with it. I liked the useful real world examples described in the book and think the book is ideal for anyone wanting to learn more about the the Social Media sites data. The material discussed in the book could have been very dry, but the author managed to keep it interesting.

The Book is available in a variety of formats (Paperback, PDF, Mobi, ePub, Kindle) from multiple sources (O'Reilly, Amazon, Apple, etc). This review was written for the O'Reilly Blogger Review program. I was not paid to write this review, but I did get to read the book for free.

In Mining the Social Web Matthew A Russell offers to instruct in identifying social connections, trends in discussion and locations by tapping into social media data. He succeeds in spades. This fast-paced and rich handbook jumps right into the fray and provides an immediate and useful exercise in accessing the Twitter API using python and doing a very quick visualisation of trending subjects. I was hooked and greedily and immediately consumed a few more of his lessons. His approach is to go direct to real world applications of why you'd want to mine data from social media such as Twitter, Buzz, Facebook and utilise other freely available tools such as Google Maps to look for patterns and present solid research findings.

As he asserts, all the user needs is some programming background and a willingness to learn basic Python tools. Fair requirements. I am no python wizard, but I found clear instructions and come away feel far more comfortable with python and available frameworks after proceeding through exercises in the book. Russell takes you through exercises step by step and provides all the instruction necessary to guide you along. The book is hands-on heavy and you do have to be willing to play along with goodly chunks of code. This is not a light browsable book. Although it does allow for some skipping about, it is the sort of volume that engages with the reader/practitioner and naturally leads you along. The exercises do assume some degree of background with data analysis. Although expressed in as simple a language as possible, certainly when getting into natural language processing for example, some background in general terms, processes and methodologies in NLP are expected. However, this should not put anyone off and I was impressed that this formed a quick and useful introduction to the craft because Russell does such a superb job of rooting the theory in everyday language and practice.

I was similarly impressed with the authors inclusion of exercises in mining your own mailbox for useful analysis and how to conduct natural language processing on blog feeds, not just sticking with the sexier and trendier flashy social media tools today. This is a very comprehensive and thoughtful approach to the avalanche of explorable data available from our social existence and Russell provides an extremely approachable and superbly crafted volume. For anyone interested in stepping beyond simple participation and taking a thoughtful view of how social media is changing our lives, this is the book of reference. What's more I really just simply enjoyed this book. Highly recommended!

Mining the Social Web by Matthew Russell is a delightful reading which covers a wide array of very pertinent social media technologies. When working in the field of social media and web mining, one comes across a plethora of buzz words, tools and techniques to do various web data analysis tasks. This book does a great job at demystifying these topics. What I liked most about Mining the Social Web is that it takes a specific area such as TF-IDF and then connects the dots with various intertwined technologies (bi-grams, graph visualization, cosine similarity) to paint the big picture in terms of practical usage. Even though there are several prerequisites (python is a big one) and a small learning curve for the tools introduced in the book, it's well written and easy to follow.

Web data analysis is quite a huge field of research and development; given it's hard to cover a majority of topics in one book with practical examples, author has done a tremendous job by addressing the practical and relevant concerns such as accessing the twitter field with oAuth, mapReduce, NLP, couchDB, processing mailboxes, accessing linkedin and last but not least the virtues of semantic web. Nowadays when most technical books usually tend to become occasional reference books where you refer to specific topic and skim through the rest, mining the social web is a big exception. Either you are trying to familiarize yourself with the current technologies of social media mining, or are a experienced developer looking for a different point of view for implementation, this book will definitely widen your horizons.

This is a great book to get you started analyzing data of various social media outlets. Russell assumes some basic Python knowledge, as he should since this isn't a tutorial for Python. With this book you are given the basic tools to breakdown the data from multiple sites, giving you the freedom to really make some interesting projects. I would definitely recommend this book to anyone with some programming knowledge interested in capitalizing on the social media market.

I have not programmed for many years and was quickly able to follow the examples and build my own programs. The examples are well designed and the sequence pulls you in to try ever more interesting analysis. I had no idea it was so easy to extract and analyze this information from so many different social web sites. It is well written. It has inspired me to learn more about the tools that are out there and to keep experimenting with the examples from the book. I look forward to future works by this author.

Just finished this book and it's all marked up with notes and highlights.

This is a good book that describes what can be done with the data from Twitter, Facebook, LikedIn and email, (the email was very interesting to me).

You do need to be open to using Python, but the author gives clear instructions and sample code is provided. Also where to get the mentioned software, how to install it, use it, and other options as well.

To be honest I am not a programmer, but a wanna-be, and this book was perfect for me. As another reviewer noted, be prepared to work with some code. Really it was easy and I think most anyone could do it, the way it is presented here.

The book is written vey well and kept my interest. Almost all the O'Reilly books I have are written this way and it is why I have bought over 2 dozen over the years.

Overall the book extends your knowledge of what you can really do with social sites beyond just using the tools the sites provide. If you are looking to dig deeper with our social networks, this is a great first book. It will show you the basics, then a little more. Afterwards if you want more it is available.