Comments about O'Reilly Media Mining the Social Web:

Mining the Social Web is a good start for anyone is going to create scripts to analyze patterns in Social Networks. I've to say that this book consider that the reader already masters Python. I think that should be written directly on the title (ie: "Mining the Social Web with Python").

I liked the really fast approach to the Social Networks, even if a lot of times I wanted more; for this reason I consider it only a start, not a complete book.

The part I really enjoyed was the one about the HTML5 microformats. This is the only book that cover the topic from the data mining point of view, as far as I've seen.

I had the pleasure to read this book in the e-book version, and I've to admit that O'Reilly did a really good job in linking the different parts of the book through hyperlinks.

The biggest part of the book is focused more on what text is important rather that why it is important. In the book is touched the why speaking about the Semantic Web processing, but this is beyond the goal of this book, I suppose.

I would recommend this book only to those people who are willing to have a starting point on the web mining or who is trying to understand if the topic is interesting for him/her.

Comments about O'Reilly Media Mining the Social Web:

The author makes a wide range of assumptions, for example that you are familiar with Linux. If you are running on Windows then parts of the book are simply not available to you. In other cases you must be familir with setting up and running Python on Linux as these details are not included.
There are also a number of areas out of date. Twitter API data has been changed considerably and these are covered in the errata on the website. In other cases, a whole chapter is dedicated to Google Buzz which no longer exists.

Comments about O'Reilly Media Mining the Social Web:

'Mining the Social Web' by Matthew Russell provides an interesting introduction into the world of data analysis using Python. There are many examples for mining many of the most popular social networks and plenty of in-depth instructions to ensure that even those with a basic understanding of programming should be able to get up and running fairly quickly. The book begins with an explanation of how to best set up the Python development tools, and then launches into tactics for the harvesting and in-depth analysis of data, generated by using social web APIs, Python tools, GitHub, HTML5, and JavaScript.

Of particular interest to myself was the chapter on Twitter, which I found to be an excellent starting point for my current research into use of the ever-growing social network. The tools provided by the book gave me with the foundation I required to move on and prepare my own analysis of the current usage trends for Twitter, and to generate usable data sets that could be visualised in an interesting and engaging fashion - providing added value to clients, particularly when analysing the content of tweets, and the patterns in relationships between users and client accounts.

Making comparisons, and then eventually value judgements based on outside factors will always be an important element of the consultancy required to assist in the explanation of data that is generated using the practices discussed in the book. However, with this book it is possible to generate a solid foundation that combined with some analytical skill and experience of the market being investigated - it is possible to achieve great results.

I would highly recommend this book to be used as both a primer, and a reference for those who are looking to begin utilising social networking for business intelligence. As we continue to live out more of our personal lives online, traditional advertising is reducing in effectiveness as potential customers are more influenced by their peers than ever. Particularly in these tough economic times, it makes sense to know your customers and their online behaviours. If you are a programmer / developer tasked with creating a solution - or a project manager looking to produce more added value for your clients, I can't urge you enough to read this book.

Comments about O'Reilly Media Mining the Social Web:

This book contains how to retrieve userdata from Twitter, LinkedIn, Google Buzz via Web API using the corresponding public python library.
Technologically, microformat, k-means clustering, Natural Language Processing, ranking algorithm, how to find similar documents are also explained by using practical SNS datas. The corresponding sections contain a summary of each algorithm.
All implementation is based on python, so readers need to understand very well beforehand.
Totally, though the explanation is summary level and doesn't dive into deeply, it is nice entry point to start to dive into text mining not only SNS but also unstructured documents.

Comments about O'Reilly Media Mining the Social Web:

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.

Comments about O'Reilly Media Mining the Social Web:

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.

Comments about O'Reilly Media Mining the Social Web:

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.