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

In this chapter, we will analyze the YouTube channel of Sony PlayStation, a very popular brand among youth, and rank their published videos in terms of popularity (views, likes, and dislikes) and also go deeper in the analysis by measuring the evolution of sentiment in terms of user comments generated for one particular video and show it on a timeline. It gives a good understanding of the popularity and attitude trends of the viewers.

This approach requires a few steps. The first step to rank videos requires us to extract the statistics, while the second requires us to gather the comments data, clean it, and calculate the sentiment measure. Finally, it will be analyzed in time-series.

This process requires us to gather both structured and unstructured data:

  • Structured data: The number of views, likes, and dislikes of all videos
  • Unstructured data: The comments generated for one video