Mastering Social Media Mining with Python

Mastering Social Media Mining with PythonReviews
Author: Marco Bonzanini
Pub Date: 2016
ISBN: 978-1-78355-201-6
Pages: 338
Language: English
Format: EPUB
Size: 10 Mb


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.
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

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.


Acquire and analyze data from all corners of the social web with Python

Table of Contents

1. Social Media, Social Data, and Python
2. #MiningTwitter – Hashtags, Topics, and Time Series
3. Users, Followers, and Communities on Twitter
4. Posts, Pages, and User Interactions on Facebook
5. Topic Analysis on Google+
6. Questions and Answers on Stack Exchange
7. Blogs, RSS, Wikipedia, and Natural Language Processing
8. Mining All the Data!
9. Linked Data and the Semantic Web