Preface

In the past few years, the popularity of social media has grown dramatically, with more and more users sharing all kinds of information through different platforms. Companies use social media platforms to promote their brands, professionals maintain a public profile online and use social media for networking, and regular users discuss about any topic. More users also means more data waiting to be mined.

You, the reader of this book, are likely to be a developer, engineer, analyst, researcher, or student who wants to apply data mining techniques to social media data. As a data mining practitioner (or practitioner-to-be), there is no lack of opportunities and challenges from this point of view.

Mastering Social Media Mining with Python will give you the basic tools you need to take advantage of this wealth of data. This book will start a journey through the main tools for data analysis in Python, providing the information you need to get started with applications such as NLP, machine learning, social network analysis, and data visualization. A step-by-step guide through the most popular social media platforms, including Twitter, Facebook, Google+, Stack Overflow, Blogger, YouTube and more, will allow you to understand how to access data from these networks, and how to perform different types of analysis in order to extract useful insight from the raw data.

There are three main aspects being touched in the book, as listed in the following list:

• Social media APIs: Each platform provides access to their data in different ways. Understanding how to interact with them can answer the questions: how do we get the data? and also what kind of data can we get? This is important because, without access to the data, there would be no data analysis to carry out. Each chapter focuses on different social media platforms and provides details on how to interact with the relevant API.
• Data mining techniques: Just getting the data out of an API doesn't provide much value to us. The next step is answering the question: what can we do with the data? Each chapter provides the concepts you need to appreciate the kind of analysis that you can carry out with the data, and why it provides value. In terms of theory, the choice is to simply scratch the surface of what is needed, without digging too much into details that belong to academic textbooks. The purpose is to provide practical examples that can get you easily started.
• Python tools for data science: Once we understand what we can do with the data, the last question is: how do we do it? Python has established itself as one of the main languages for data science. Its easy-to-understand syntax and semantics, together with its rich ecosystem for scientific computing, provide a gentle learning curve for beginners and all the sharp tools required by experts at the same time. The book introduces the main Python libraries used in the world of scientific computing, such as NumPy, pandas, NetworkX, scikit-learn, NLTK, and many more. Practical examples will take the form of short scripts that you can use (and possibly extend) to perform different and interesting types of analysis over the social media data that you have accessed.

If exploring the area where these three main topics meet is something of interest, this book is for you.