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


Sentiment analysis and entity recognition are two powerful social media analytics techniques to get context around user content. Sports being a sentiment and emotion inciting subject among audiences, for this chapter the dataset we used were tweets using the Twitter API on the English Football Premier League. We used the Twitter REST and Streaming API to collect the data and also applied basic cleaning explained in Chapter 2,Harnessing Social Data - Connecting, Capturing, and Cleaning) and new cleaning methods such as device detection from Twitter API metadata. Sentiment Analysis allows us to categorize text into positive, negative, and neutral categories. We also learnt that there are limitations to sentiment analysis with accuracy, especially in ambiguous expressions. We used the VADER (Valence Aware Dictionary for Sentiment Reasoning) module from NLTK for sentiment analysis. We also saw that we can build our own sentiment analysis algorithm through machine learning on test and...