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 last decade has seen an enormous growth of social media platforms, such as Facebook, Twitter, and Youtube. Since 2009, another platform with a different format and objective has grown - Pinterest. Unlike conventional social media, which is used as a communication tool, Pinterest is described as a "catalog of ideas". It allows users to create pinboards, and to organize and share content over the web, based on interests and ideas.

We've explored the use of the Pinterest API and also advanced scraping techniques, using Selenium and BeautifulSoup, to gather data for learning purposes. However, these have time constraints to be scalable. Therefore, we extracted the data from our own pinboard using the endpoints (user, board, and pins) and the search results on the topic of fashion. For data analysis, we used bigram analysis to extract a list of topics on our own pins and then visualized it in a graph structure using NetworkX (via fruschterman_reingold_layout). We then studied centrality...