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

Data processing


In the previous step we structured the raw data which is now ready for further analysis. Our objective is to analyze two types of data:

  • Textual data in description
  • Numerical data in other variables

Each of them requires a different pre-processing technique. Let's take a look at each type in detail.

Textual data

For the first kind, we have to create a new variable which contains a cleaned string. We will do it in three steps which have already been presented in previous chapters:

  • Selecting English descriptions
  • Tokenization
  • Stopwords removal

As we work only on English data, we should remove all the descriptions which are written in other languages. The main reason to do so is that each language requires a different processing and analysis flow. If we left descriptions in Russian or Chinese, we would have very noisy data which we would not be able to interpret. As a consequence, we can say that we are analyzing trends in the English-speaking world.

Firstly, we remove all the empty strings...