Mastering Social Media Mining with Python
Marco Bonzanini
- 338 pages
- English
- ePUB (mobile friendly)
- Available on iOS & Android
Mastering Social Media Mining with Python
Marco Bonzanini
About This Book
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
Who This Book Is For
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
In Detail
Your social media is filled with a wealth of hidden data – unlock it with the power of Python. Transform your understanding of your clients and customers when you use 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. Explore the Python libraries used for social media mining, and get the tips, tricks, and insider insight you need to make the most of them. Discover how to develop data mining tools that use a social media API, and how to create your own data analysis projects using Python for clear insight from your social data.
Style and approach
This practical, hands-on guide will help you learn everything you need to perform data mining for social media. Throughout the book, we take an example-oriented approach to use Python for data analysis and provide useful tips and tricks that you can use in day-to-day tasks.
Frequently asked questions
Information
Mastering Social Media Mining with Python
Mastering Social Media Mining with Python
Credits
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 |
About the Author
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.
About the Reviewer
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Preface
- 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 so...