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

Combining NER and sentiment analysis


In order to get insightful information we'll calculate the sentiment for the most frequent entities related to football clubs. We take the three most mentioned clubs and check the mean sentiment for each of them using the np.mean() function from numpy as follows:

subset = dataset[dataset['tweet'].str.contains('Liverpool')] 
avg_sentiment = np.mean(subset['sentiment'])

We obtain the following results illustrated by some random verbatim:

  • Liverpool 0.1166: Milner focused on Liverpool results #SSFootball via @SuperSportTV https://t.co/CIthkFY5Qs. Juninho says he is delighted Liverpool forward Philippe Coutinho replaced him as the top-scoring Brazilian in the Premier League. African striker on his love for Liverpool. https://t.co/Mfk6wXWwhf

Similarly, applying the other two keywords we get the following results:

  • Chelsea 0.2121: Melo melo@ChelseaFansUSA: Zouma: One of the best memories I have from my time at Chelsea so far was my first goal in the Premier League...