Social media is an amazing source of data just waiting to be analysed. This book provides helpful examples of how to mine a variety of data sources using Python, a powerful programming language that simplifies access to the social networking APIs.
Each chapter has an example IPython Notebook, an interactive Python interpreter with a notebook like experience to make it even easier to follow along with the examples in the book and customise the code for your own purposes. Because the Python development environment can be a bit complicated to set up, the author has provided a virtual machine configured with the IPython Notebook and all the libraries used in the book already. A short video and instructions explain how to set it up, and relatively quickly you have some example code ready to execute and customise without too much bother.
The book is extensive with coverage of the social platforms, their APIs, how to extract data from them, example analyses of the data, formatting, creating graphs from the data, clustering, text mining, natural language processing, and more. Each chapter is used to introduce the social platform including the purpose and types of data, the technicalities of both the APIs to access the data, and the technical details of the data mining techniques.Platforms covered include Twitter, Facebook, LinkedIn, Google+, web pages, mail boxes, GitHub, and the Semantic web.
You don't have to be a Python programmer to get value out of this book, anyone with programming experience should be able to understand the syntax together with the details provided in the book. The IPython notebook makes it easy to experiment with the existing code to try out your own data extractions and analyses. This comprehensive book will be of value to anyone interested in data mining, `big data', and analysing data from social media in particular.