| Mining the Social Web |
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Author: Matthew A. Russell The social web provides many opportunities for data mining. This book is fun but not for the faint hearted.
Author: Matthew A. Russell I never fail to be amazed at the range of high tech things that Python is used for. This particular book is all about data mining as applied to the massive amounts of data generated every day by the social web. Be warned however that this is a fast paced and technical view of a great many topics. it isn't for the faint hearted but it is a lot of fun and who knows it might even be useful.
The first chapter starts off with how to install Python and how to install the necessary packages. The first example, to make sure it is all working, is a simple Twitter data collection application complete with some simple data analysis - frequency, lexical diversity and drawing graphs. The actual level of Python used throughout the book is never over-complex and if you can't program in Python then as long as you can program in something you should be able to get value from the examples. Chapter 2 is on microformats and using them to extract data from web pages - geolocation, recipes and reviews. The next chapter takes a step into the world of email which you might not think of as social media in the modern sense - but the data is still there to mine! This also introduces CouchDB , Lucene and Restful web services. Chapter 4 returns to Twitter and goes much deeper into processing the sort of data that you can get from Twitter. A lot of this comes across as very ad-hoc rather than a worked out approach but this probably corresponds to the nature of the work. The later part of the chapter focuses on analysing social networks - clique detection and graph theory. The next chapter continues working with Twitter. This is another ad-hoc analysis but in this case of Tim O'Reilly's tweets. Later we have a graphic indicating tweet frequency as a tag cloud. Chapter 6 moves on to consider LinkedIn which in many ways has a more direct connection with making a profit. The focus of the chapter is on cluster analysis and this is a fairly classical approach. Chapter 7 changes gear a little and looks at the analysis of texts rather than structures and relationships. It introduces some natural language processing with NLTK which continues into the following chapters on processing blogs. This is the toughest problem tackled in the book and of course it doesn't present a complete solution but it is full of ideas. Chapter 9 finally reaches Facebook which is perhaps presents the most diverse of the social data on offer being a mix of blogging, images, micro blogging and so on. The book rounds off with a chat about the semantic web - with no definite conclusions. This is an interesting introduction to accessing social data and it presents lots of different ways of working with it and displaying it. I have to admit that I enjoyed reading it but there were times that I wondered why exactly we were doing something. Yes fun but is it useful? Perhaps the very fact that I'm asking this question suggests that I'm not in the last analysis the ideal reader for this book - even so, a few examples that were really useful would have made me feel happier. A really enjoyable book and highly recommended to the appropriate reader.
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| Last Updated ( Monday, 16 May 2011 ) |