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


The avalanche of social network data is a result of communication platforms being developed for the last two decades. These are the platforms that evolved from chat rooms to personal information sharing and finally, social and professional networks. Among many, Facebook, Twitter, Instagram, Pinterest, and LinkedIn have emerged as the modern day social media. These platforms collectively have reach of more than a billion individuals across the world, sharing their activities and interaction with each other. Sharing of their data by these media through APIs and other technologies has given rise to a new field called social media analytics. This has multiple applications, such as in marketing, personalized recommendations, research, and societal. modern data science techniques such as machine learning and text mining are widely used for these applications. Python is one of the most programming languages used for these techniques. However, manipulating the unstructured-data from social networks requires a lot of precise processing and preparation before coming to the most interesting bits.

In the next chapter, we will see the way this data from social networks can be harnessed, processed, and prepared to make a sandbox for interesting analysis and applications in the subsequent chapters.