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


Data is the main asset for any social network. Yet StackExchange does a wonderful job of exposing its data for exploration and analysis. Unlike other social networks, which expose their data through APIs mostly and restrict many details, StackExchange not only provides multiple channels like data dumps and data explorer apart from APIs, but it also provides access to an almost complete set of public information.

That being said, there are challenges while working with a platform such as StackExchange. The following are a few of them:

  • Data dumps expose the data in the form of XML files. Though there are parsers available in R for using XML data, there is an inherent limit imposed if the XML files are huge (StackOverflow's XML files amount to 30 GB). This limitation can be overcome by first loading the data into a local database such as MySQL and then working upon the required subset of data.

  • Data explorer has row limits imposed upon the data extracted through the explorer (current...