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


Foursquare is an important social media source. The concept of check-in that Foursquare introduced was quite revolutionary at the time of launching. Now we find the check-in feature in Facebook also. This chapter helped us in understanding the basic flow of data extraction in absence of a neatly developed R package. We learned how to get creative with our data extraction process. We built up on the key concepts of sentiment and graph analytics introduced in previous chapters. A conscious effort was made to not repeat the obvious processes again but to build on those processes for building something relevant. The most important takeaway from this chapter is to learn how to form a problem definition. We introduced it with an example (recommendation engine) and the users are encouraged to find different such problems in the data that we extracted (hint: classification system). This chapter also prepared us for the upcoming challenges. The next social media sources are going to be challenging...