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Learning Social Media Analytics with R

You're reading from Learning Social Media Analytics with R Transform data from social media platforms into actionable business insights

Product type Paperback

Published in May 2017

Publisher Packt

ISBN-13 9781787127524

Length 394 pages

Edition 1st Edition

Languages

R

Tools

GitHub

Concepts

Data Analysis

Authors (4):

Sarkar

Karthik Ganapathy

Raghav Bali

Sharma

View More author details

Table of Contents (16) Chapters

Learning Social Media Analytics with R

Credits

About the Author

About the Reviewer

www.PacktPub.com

Customer Feedback

Preface

1. Getting Started with R and Social Media Analytics FREE CHAPTER

2. Twitter – What's Happening with 140 Characters

3. Analyzing Social Networks and Brand Engagements with Facebook

4. Foursquare – Are You Checked in Yet?

5. Analyzing Software Collaboration Trends I – Social Coding with GitHub

6. Analyzing Software Collaboration Trends II - Answering Your Questions with StackExchange

7. Believe What You See – Flickr Data Analysis

8. News – The Collective Social Media!

Index

Summary

The virtual world of the Internet is evolving at breakneck speeds and it is the developers/programmers/innovators that are driving it in the background. GitHub and StackExchange are the two most popular social networks leveraged by this community to share, help and expand their knowledge.

As discussed in the previous chapter on GitHub, there are some strong trends and insights clearly visible from the data available. Building upon the same, this chapter dealt with data related to StackExchange.

We started off with understanding the humble beginnings of this platform followed by ways of getting our hands upon its data. This was followed by utilizing the data dumps as our primary source of data from the StackExchange platform. The next step was to understand what data is available to us and how it is organized into separate files. We then utilized the power of R to use this data and extract some interesting insights related to programming languages, demographics, and so on from the data...

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Authors (4)

Sarkar

Sarkar

Dipanjan (DJ) Sarkar is a Data Scientist at Intel, leveraging data science, machine learning, and deep learning to build large-scale intelligent systems. He holds a master of technology degree with specializations in Data Science and Software Engineering. He has been an analytics practitioner for several years now, specializing in machine learning, NLP, statistical methods, and deep learning. He is passionate about education and also acts as a Data Science Mentor at various organizations like Springboard, helping people learn data science. He is also a key contributor and editor for Towards Data Science, a leading online journal on AI and Data Science. He has also authored several books on R, Python, machine learning, NLP, and deep learning.

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Karthik Ganapathy

Karthik Ganapathy

Contacted for Reviewing

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Raghav Bali

Raghav Bali

Raghav Bali is a Staff Data Scientist at Delivery Hero, a leading food delivery service headquartered in Berlin, Germany. With 12+ years of expertise, he specializes in research and development of enterprise-level solutions leveraging Machine Learning, Deep Learning, Natural Language Processing, and Recommendation Engines for practical business applications. Besides his professional endeavors, Raghav is an esteemed mentor and an accomplished public speaker. He has contributed to multiple peer-reviewed papers and authored multiple well received books. Additionally, he holds co-inventor credits on multiple patents in healthcare, machine learning, deep learning, and natural language processing.

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Sharma

Sharma

Tushar Sharma has a master's degree specializing in data science from the International Institute of Information Technology, Bangalore. He works as a data scientist with Intel. In his previous job he used to work as a research engineer for a financial consultancy firm. His work involves handling big data at scale generated by the massive infrastructure at Intel. He engineers and delivers end to end solutions on this data using the latest machine learning tools and frameworks. He is proficient in R, Python, Spark, and mathematical aspects of machine learning among other things. Tushar has a keen interest in everything related to technology. He likes to read a wide array of books ranging from history to philosophy and beyond. He is a running enthusiast and likes to play badminton and tennis.

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