Marco Bonzanini - Mastering Social Media Mining with Python
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Acquire and analyze data from all corners of the social web with Python
About This Book- Make sense of highly unstructured social media data with the help of the insightful use cases provided in this guide
- Use this easy-to-follow, step-by-step guide to apply analytics to complicated and messy social data
- This is your one-stop solution to fetching, storing, analyzing, and visualizing social media data
This book is for intermediate Python developers who want to engage with the use of public APIs to collect data from social media platforms and perform statistical analysis in order to produce useful insights from data. The book assumes a basic understanding of the Python Standard Library and provides practical examples to guide you toward the creation of your data analysis project based on social data.
What You Will Learn- Interact with a social media platform via their public API with Python
- Store social data in a convenient format for data analysis
- Slice and dice social data using Python tools for data science
- Apply text analytics techniques to understand what people are talking about on social media
- Apply advanced statistical and analytical techniques to produce useful insights from data
- Build beautiful visualizations with web technologies to explore data and present data products
Python is the programming language of choice for data scientists to prototype, visualize, and run data analyses on small- and medium-sized data sets. Countless businesses are turning to Python to solve the problems of understanding consumer behavior and turning raw data into actionable customer insights.
This book will help you acquire and analyze data from leading social media sites. It will show you how to employ scientific Python tools to mine popular social websites such as Facebook, Twitter, Quora, and more.
We will explore the Python libraries and cover each aspect of social media mining. We will teach you to develop data mining tools that use a social media API and how to create your own data analysis projects using Python.
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Copyright 2016 Packt Publishing
All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.
Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this book.
Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.
First published: July 2016
Production reference: 1260716
Published by Packt Publishing Ltd.
Livery Place
35 Livery Street
Birmingham
B3 2PB, UK.
ISBN 978-1-78355-201-6
www.packtpub.com
Author Marco Bonzanini | Copy Editor Vibha Shukla |
Reviewer Weiai Wayne Xu | Project Coordinator Nidhi Joshi |
Commissioning Editor Pramila Balan | Proofreader Safis Editing |
Acquisition Editor Sonali Vernekar | Indexer Mariammal Chettiyar |
Content Development Editor Siddhesh Salvi | Graphics Jason Monteiro Disha Haria |
Technical Editor Pranil Pathare | Production Coordinator Arvindkumar Gupta |
Marco Bonzanini is a data scientist based in London, United Kingdom. He holds a PhD in information retrieval from Queen Mary University of London. He specializes in text analytics and search applications, and over the years, he has enjoyed working on a variety of information management and data science problems.
He maintains a personal blog at http://marcobonzanini.com, where he discusses different technical topics, mainly around Python, text analytics, and data science.
When not working on Python projects, he likes to engage with the community at PyData conferences and meet-ups, and he also enjoys brewing homemade beer.
This book is the outcome of a long journey that goes beyond the mere content preparation. Many people have contributed in different ways to shape the final result. Firstly, I would like to thank the team at Packt Publishing, particularly Sonali Vernekar and Siddhesh Salvi, for giving me the opportunity to work on this book and for being so helpful throughout the whole process. I would also like to thank Dr. Weiai Wayne Xu for reviewing the content of this book and suggesting many improvements. Many colleagues and friends, through casual conversations, deep discussions, and previous projects, strengthened the quality of the material presented in this book. Special mentions go to Dr. Miguel Martinez-Alvarez, Marco Campana, and Stefano Campana. I'm also happy to be part of the PyData London community, a group of smart people who regularly meet to talk about Python and data science, offering a stimulating environment. Last but not least, a distinct special mention goes to Daniela, who has encouraged me during the whole journey, sharing her thoughts, suggesting improvements, and providing a relaxing environment to go back to after work.
Weiai Wayne Xu is an assistant professor in the department of communication at University of Massachusetts Amherst and is affiliated with the Universitys Computational Social Science Institute. Previously, Xu worked as a network science scholar at the Network Science Institute of Northeastern University in Boston. His research on online communities, word-of-mouth, and social capital have appeared in various peer-reviewed journals. Xu also assisted four national grant projects in the area of strategic communication and public opinion. Aside from his professional appointment, he is a co-founder of a data lab called CuriosityBits Collective (http://www.curiositybits.org/).
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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.
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