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.
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.
Check it out at O'Reilly's web.
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