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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):

Raghav Bali

Dipanjan Sarkar

Karthik Ganapathy

Tushar Sharma

View More author details

Table of Contents (10) Chapters

Preface

1. Getting Started with R and Social Media Analytics

2. Twitter – What's Happening with 140 Characters FREE CHAPTER

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

Challenges to news data analysis

The analysis of news data was probably one of the most challenging tasks in this book. We will try to give the reader a summary of the toughest problems that we encountered in the process of developing this chapter:

Lack of API sources: News data providers are not always very API friendly. We were lucky to have a prestigious source such as The Guardian, which believes in open access to its data and goes to great lengths to ensure that. But, apart from a couple of big names such as The New York Times and The Guardian, we won't find a lot of data providers going down the API route.
Web scraping: Web scraping HTML data for text is quite a complex process. Once again, we were lucky that the HTML structure for our data sources was quite simple. A more involved structure would have meant a larger and more elaborate process of data scraping. (We encourage the reader to take a look at the HTML structure for any New York Times article to realize the complexity that...

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Authors (4)

Karthik Ganapathy

Karthik Ganapathy

Contacted for Reviewing

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Sarkar

Sarkar

Aurobindo Sarkar leads a team of data scientists and engineers at Session AI, developing cloud-based ML models for in-session marketing in e-commerce and retail. As a former CTO at multiple SaaS startups, he has architected secure, scalable, and highly available AWS cloud applications. His research interests now focus on AWS-based large-scale transformer models for NLP and HFT models for the futures and options market. Aurobindo holds a bachelor's degree in engineering from IIT Delhi, a master's in management from the Indian Institute of Science Bangalore, and a master's in computer science from New York University.

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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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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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