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 Science and StackExchange

Data science is not just an industry buzzword but an actual field of study which encompasses a whole lot of academic research and industry level application of these concepts. The is one of those sites where users from different backgrounds and levels of expertise ask questions and discuss a whole lot of interesting concepts and things related to the field of data science, machine learning, advanced analytics, and so on.

As part of this use case, we will be making use of the Posts.xml file primarily from the said site for the analysis and uncovering of insights. Introduced in the previous section, we will utilize the same utility to load the XML and perform a couple of pre-processing steps, such as date-time cleanup to get our dataset in useable form. The following snippet performs the cleanup as well as brings the Tags attribute into useable form:

PostsDF <- loadXMLToDataFrame(paste0(path,"Posts.xml"))

# change data...