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

Understanding interestingness – similarities

We started off this chapter discussing Flickr and its interestingness algorithm: the enigma of an algorithm which brings forth some amazing photos for us to explore and enjoy from millions uploaded every day. The Explore page presents a pretty diverse set of photos showcasing different photography styles, clicked using different photography equipment by photographers of varied skills. Yet the interestingness algorithm picks them all!

Through this use case we will try to find out what type of photos are being picked up by the algorithm and understand/uncover if there are certain patterns or similarities between such interesting photos.

Since we do not know much about the algorithm other than the fact that it presents excellent photos to explore, we'll take the unsupervised approach to see what we have here.

Under the unsupervised umbrella of machine-learning algorithms, one of the simplest and most widely used algorithms to get started with is the...