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


News data is a very important data source as it gives us a collective glimpse of the major themes in our day-to-day lives. We have witnessed how it can be a difficult process to collect news data and do some text mining on it. We have understood the basic concepts of web scraping, which is required in most data collections from the public domain. We have learned about the various problems we can have with textual data and how to work around them. An important point to mention about this chapter is the importance of maintaining an unbiased point of view while analyzing text data. Otherwise, it is very easy for text data mining to denigrate into a bad case of selection bias. Text data analysis is very diverse, a rapidly developing area of research, and tough to contain in one chapter. We encourage our readers to explore different text mining tools and find out what different use cases they can build on the datasets that we collected; this will certainly make for an interesting exercise...