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


A tweet is far more than just 140 characters, and Twitter offers quite a lot to play with for a social network. We covered a lot of ground in this chapter by looking at many concepts and solving use cases based on real Twitter data. We learned about different Twitter objects and its APIs. We created an app of our own and utilized R's twitteR package to connect and tap into its APIs. We performed trend analysis to understand how a hashtag is used by tweeple and its temporal affects. We also solved a use case involving sentiment analysis. Through this use case, we first understood the key concepts related to sentiment analysis and then employed them to understand what emotions @POTUS conveys through his tweets. We also performed hierarchical clustering of tweets to visualize common themes using a dendrogram. The final use case analyzed Twitter from a network/graph analysis stand point. We utilized R's different libraries to prepare a network map of followers and perform analysis over...