Book Image

Python Social Media Analytics

By : Baihaqi Siregar, Siddhartha Chatterjee, Michal Krystyanczuk
Book Image

Python Social Media Analytics

By: Baihaqi Siregar, Siddhartha Chatterjee, Michal Krystyanczuk

Overview of this book

Social Media platforms such as Facebook, Twitter, Forums, Pinterest, and YouTube have become part of everyday life in a big way. However, these complex and noisy data streams pose a potent challenge to everyone when it comes to harnessing them properly and benefiting from them. This book will introduce you to the concept of social media analytics, and how you can leverage its capabilities to empower your business. Right from acquiring data from various social networking sources such as Twitter, Facebook, YouTube, Pinterest, and social forums, you will see how to clean data and make it ready for analytical operations using various Python APIs. This book explains how to structure the clean data obtained and store in MongoDB using PyMongo. You will also perform web scraping and visualize data using Scrappy and Beautifulsoup. Finally, you will be introduced to different techniques to perform analytics at scale for your social data on the cloud, using Python and Spark. By the end of this book, you will be able to utilize the power of Python to gain valuable insights from social media data and use them to enhance your business processes.
Table of Contents (17 chapters)
Title Page
About the Authors
About the Reviewer
Customer Feedback

Scope and process

GitHub API allows us to get information about public code repositories submitted by users. It covers lots of open-source, educational and personal projects. Our focus is to find the trending technologies and programming languages of last few months, and compare with repositories from past years. We will collect all the meta information about the repositories,

  • Name: The name of the repository
  • Description: A description of the repository
  • Watchers: People following the repository and getting notified about its activity
  • Forks: Users cloning the repository to their own accounts
  • Open Issues: Issues submitted about the repository

We will use this data, a combination of qualitative and quantitative information, to identify the most recent trends and weak signals. The process can be represented by the steps shown in the following figure: