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


Ten or twenty years ago, we did not need to scale up, except in very specific domains. Today, with the boom of digital, data volume is increasing exponentially. In today's world we need to be able to scale. Scaling brings about more new challenges than simple sequential programming, but its benefits largely outweigh the challenges.

Social media analytics also require the processing and analysis of massive amounts of unstructured data, so the ability to scale our algorithms and analysis is indispensable.

In this chapter, we looked at the basic methods of speeding up programs, like multi-threading and multi-processing. These methods are great when we have a powerful machine and a moderate sized data. If we are working on a small machine with, for example, four to eight cores then we will be limited on the extent to which we can parallelize our code. However, of course, if we only have a single machine with such resources, installing Spark on it is pointless. At the same time, let's say...