Book Image

Learning Social Media Analytics with R

By : Dipanjan Sarkar, Karthik Ganapathy, Raghav Bali, Tushar Sharma
Book Image

Learning Social Media Analytics with R

By: Dipanjan Sarkar, Karthik Ganapathy, Raghav Bali, Tushar Sharma

Overview of this book

The Internet has truly become humongous, especially with the rise of various forms of social media in the last decade, which give users a platform to express themselves and also communicate and collaborate with each other. This book will help the reader to understand the current social media landscape and to learn how analytics can be leveraged to derive insights from it. This data can be analyzed to gain valuable insights into the behavior and engagement of users, organizations, businesses, and brands. It will help readers frame business problems and solve them using social data. The book will also cover several practical real-world use cases on social media using R and its advanced packages to utilize data science methodologies such as sentiment analysis, topic modeling, text summarization, recommendation systems, social network analysis, classification, and clustering. This will enable readers to learn different hands-on approaches to obtain data from diverse social media sources such as Twitter and Facebook. It will also show readers how to establish detailed workflows to process, visualize, and analyze data to transform social data into actionable insights.
Table of Contents (16 chapters)
Learning Social Media Analytics with R
About the Author
About the Reviewer
Customer Feedback


This chapter presented an opportunity to learn about a social network which has so far passed the test of time. Flickr has evolved with the Internet and has been, for a long time now, a popular site for professional and amateur photographers alike. Since its very inception, Flickr has been part of numerous research studies and their APIs have provided analysts/researchers access to their data. With about 10 billion photos, Flickr is a goldmine of sorts. In this chapter, we covered ways of interacting with Flickr using its APIs, and prepared some useful utilities to extract and preprocess the deeply nested response objects. We learnt a few basics related to photography, in particular EXIF, and used this domain knowledge to cluster our set of extracted photos using the K-means clustering algorithm. We also utilized photo attributes such as iso, focal_length, views, and so on, to build a random forest based classifier to help us identify whether a photo will land on the Explore page...