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


The virtual world of the Internet is evolving at breakneck speeds and it is the developers/programmers/innovators that are driving it in the background. GitHub and StackExchange are the two most popular social networks leveraged by this community to share, help and expand their knowledge.

As discussed in the previous chapter on GitHub, there are some strong trends and insights clearly visible from the data available. Building upon the same, this chapter dealt with data related to StackExchange.

We started off with understanding the humble beginnings of this platform followed by ways of getting our hands upon its data. This was followed by utilizing the data dumps as our primary source of data from the StackExchange platform. The next step was to understand what data is available to us and how it is organized into separate files. We then utilized the power of R to use this data and extract some interesting insights related to programming languages, demographics, and so on from the data...