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You're reading from Python Social Media Analytics Analyze and visualize data from Twitter, YouTube, GitHub, and more

Product type Paperback

Published in Jul 2017

Publisher Packt

ISBN-13 9781787121485

Length 312 pages

Edition 1st Edition

Languages

Python

Tools

GitHub

Concepts

Data Analysis

Authors (3):

Baihaqi Siregar

Siddhartha Chatterjee

Michal Krystyanczuk

View More author details

Table of Contents (17) Chapters

Title Page

Credits

About the Authors

Acknowledgments

About the Reviewer

www.PacktPub.com

Customer Feedback

Preface

1. Introduction to the Latest Social Media Landscape and Importance FREE CHAPTER

2. Harnessing Social Data - Connecting, Capturing, and Cleaning

3. Uncovering Brand Activity, Popularity, and Emotions on Facebook

4. Analyzing Twitter Using Sentiment Analysis and Entity Recognition

5. Campaigns and Consumer Reaction Analytics on YouTube – Structured and Unstructured

6. The Next Great Technology – Trends Mining on GitHub

7. Scraping and Extracting Conversational Topics on Internet Forums

8. Demystifying Pinterest through Network Analysis of Users Interests

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

Summary

In recent years, GitHub has emerged as the most well-known coding platform. With 20 million users and 57 million repositories, it's the most extensively used version control system. The social networking features provided by the platform for code repositories is also termed Social Coding. The API provided by GitHub allows you to perform an interesting analysis on data. Using the GitHub API, we used a combination of textual descriptions and other numerical data to figure out the latest trending technologies. The numerical data we extracted included watchers, open issues, forks, and repository size. Text analytics of the description using bi-grams gave an interesting list of technologies that are the most popular, mainly Artificial Intelligence technologies like deep learning and TensorFlow, and also a mix of open source projects and various diverse repositories related to software engineering. Analysis of the programming languages for these repositories showed us that Python and SWIFT...