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
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
Index

Category trend analysis


Foursquare is essentially a collection of real-life locations databases and their interaction with the actual users of those locations. How the users interact with these venues is the kind of data which encapsulates a lot of interesting information, both about the venues and the users. For our opening analysis into the Foursquare data, we will try to answer some questions about the choices of users in different cities across the world. We will learn how to extract data relevant to our analysis, how to ask the relevant questions and answer them using visualizations, and lastly, how to fit a usable analytics use case around the data. So let's dive in!

Getting the data – the usual hurdle

We want to get check-in data for some important cities across the globe and then use that data to find out what are the category trends are in those cities. Then we will proceed further with that data and try to build a recommender system which will tell us which restaurant category to...