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

Chapter 9. Social Data Analytics at Scale – Spark and Amazon Web Services

In the age of big data we have to handle new problems for data handling that did not exist before in terms of the three Vs (volume, variety, and velocity). When we handle very large amounts of data, we have to change our approach entirely. For example, algorithms can no longer use exhaustive brute force, because this approach might just take years to complete. Instead, we would use intelligent filtering to reduce the search space. Another example is when we have very high dimensions; for example, in text analysis, where every word or combination of words in the vocabulary constitute a dimension we need to change algorithms to adapt to such scenarios.

Advances in cluster computing have given us a new tool to handle the challenge of big data. No longer do we think of performing an analysis on a single node (your computer), we have progressed to thinking in terms of clusters of resources. Of course, cluster systems existed...