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
Credits
About the Authors
Acknowledgments
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

Analysis


Now that we have defined precisely the process and the techniques that we apply, we will move to the actual exercise of generating the code for the data analysis life cycle of extracting the data, storing it, cleaning it, and applying text mining techniques to analyze the content on the Facebook page of both Google and its users.

Step 1 -€“ data extraction

In the first step, we will define Facebook endpoints, which will be used to retrieve the data from Facebook. We need two different endpoints in order to be able to extract all the posts and comments from the Google Facebook page. Creation of an access token was explained in Chapter 2, Harnessing Social Data - Connecting, Capturing, and Cleaning and it is a prerequisite for connection to the Graph API. The access token should be stored in a dictionary params under a key access token.

The first endpoint will be used to extract all the posts:

page_url = 'https://graph.facebook.com/v2.8/Google/feed?fields=id,message,reactions,shares,from...