Deploy cutting-edge sentiment analysis techniques to real-world social media data using R with Packt's new book and eBook

May 2014 | Open Source

Packt is pleased to announce the release of its new book Social Media Mining with R. This is a concise, hands-on guide with many practical examples and an in-depth exploration of inference and social science research, which will help you with mining data in the real world. The book has 88 pages and is priced at $27.99. It is also available in all the popular formats, such as Kindle and selected library formats, for $14.44.

About the Authors: 

Richard Heimann leads the Data Science Team at Data Tactics Corporation and is an EMC Certified Data Scientist specializing in spatial statistics, data mining, big data, and pattern discovery and recognition. Richard is an adjunct faculty member at the University of Maryland, Baltimore County, where he teaches spatial analysis and statistical reasoning. Additionally, he is an instructor at George Mason University, teaching human terrain analysis, and is also a selection committee member for the 2014-2015 AAAS Big Data and Analytics Fellowship Program. He has recently assisted DARPA, DHS, the US Army, and the Pentagon with analytical support. 

Nathan Danneman holds a PhD degree from Emory University, where he studied International Conflict. Recently, his technical areas of research have included the analysis of textual and geospatial data and the study of multivariate outlier detection. Nathan is currently a data scientist at Data Tactics, and supports programs at DARPA and the Department of Homeland Security.

R is a free software programming language and software environment for statistical computing and graphics. The R language is widely used among statisticians and data miners to develop statistical software and data analysis. 

Social Media Mining with R provides detailed instructions on how to obtain, process, and analyze a variety of socially-generated data while providing a theoretical background to help accurately interpret the findings. Readers will be shown R code and examples of data that can be used as a springboard. The book begins with an introduction to social media data, including its sources and properties. Following on from this, readers will learn the basics of R programming in a straightforward, unassuming way. 

Social Media Mining with R covers the following topics:

Chapter 1: Going Viral
Chapter 2: Getting Started with R
Chapter 3: Mining Twitter with R
Chapter 4: Potentials and Pitfalls of Social Media Data
Chapter 5: Social Media Mining - Fundamentals
Chapter 6: Social Media Mining - Case Studies 

This book is targeted at programmers who wish to get hands-on experience working with social data from the Web.

Social Media Mining with R
Deploy cutting-edge sentiment analysis techniques to real-world social media data using R

For more information, please visit book page

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