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


In recent years, GitHub has emerged as the most well-known coding platform. With 20 million users and 57 million repositories, it's the most extensively used version control system. The social networking features provided by the platform for code repositories is also termed Social Coding. The API provided by GitHub allows you to perform an interesting analysis on data. Using the GitHub API, we used a combination of textual descriptions and other numerical data to figure out the latest trending technologies. The numerical data we extracted included watchers, open issues, forks, and repository size. Text analytics of the description using bi-grams gave an interesting list of technologies that are the most popular, mainly Artificial Intelligence technologies like deep learning and TensorFlow, and also a mix of open source projects and various diverse repositories related to software engineering. Analysis of the programming languages for these repositories showed us that Python and SWIFT...