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Danneman Nathan - Social media mining with R

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Danneman Nathan Social media mining with R

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Deploy cuttingedge sentiment analysis techniques to realworld social media data using RAbout This Book
  • Learn how to face the challenges of analyzing social media data
  • Get hands-on experience with the most common, up-to-date sentiment analysis tools and apply them to data collected from social media websites through a series of in-depth case studies, which includes how to mine Twitter data
  • A focused guide to help you achieve practical results when interpreting social media data
Who This Book Is For

Whether you are an undergraduate who wishes to get hands-on experience working with social data from the Web, a practitioner wishing to expand your competencies and learn unsupervised sentiment analysis, or you are simply interested in social data analysis, this book will prove to be an essential asset. No previous experience with R or statistics is required, though having knowledge of both will enrich your experience.

What You Will Learn
  • Learn the basics of R and all the data types
  • Explore the vast expanse of social science research
  • Discover more about data potential, the pitfalls, and inferential gotchas
  • Gain an insight into the concepts of supervised and unsupervised learning
  • Familiarize yourself with visualization and some cognitive pitfalls
  • Delve into exploratory data analysis
  • Understand the minute details of sentiment analysis
In Detail

The growth of social media over the last decade has revolutionized the way individuals interact and industries conduct business. Individuals produce data at an unprecedented rate by interacting, sharing, and consuming content through social media. However, analyzing this ever-growing pile of data is quite tricky and, if done erroneously, could lead to wrong inferences.

By using this essential guide, you will gain hands-on experience with generating insights from social media data. This book provides detailed instructions on how to obtain, process, and analyze a variety of socially-generated data while providing a theoretical background to help you accurately interpret your findings. You will be shown R code and examples of data that can be used as a springboard as you get the chance to undertake your own analyses of business, social, or political data.

The book begins by introducing you to the topic of social media data, including its sources and properties. It then explains the basics of R programming in a straightforward, unassuming way. Thereafter, you will be made aware of the inferential dangers associated with social media data and how to avoid them, before describing and implementing a suite of social media mining techniques.

Social Media Mining in R provides a light theoretical background, comprehensive instruction, and state-of-the-art techniques, and by reading this book, you will be well equipped to embark on your own analyses of social media data.

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Social Media Mining with R

Social Media Mining with R

Copyright 2014 Packt Publishing

All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.

Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the authors, nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this book.

Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.

First published: March 2014

Production Reference: 1180314

Published by Packt Publishing Ltd.

Livery Place

35 Livery Street

Birmingham B3 2PB, UK.

ISBN 978-1-78328-177-0

www.packtpub.com

Cover Image by Monse G. Wood (<>)

Credits

Authors

Nathan Danneman

Richard Heimann

Reviewers

Carlos J. Gil Bellosta

Vibhav Vivek Kamath

Feng Mai

Ajay Ohri

Yanchang Zhao

Acquisition Editors

Martin Bell

Subho Gupta

Richard Harvey

Luke Presland

Content Development Editor

Rikshith Shetty

Technical Editors

Arwa Manasawala

Ankita Thakur

Copy Editors

Sarang Chari

Gladson Monteiro

Adithi Shetty

Project Coordinator

Sageer Parkar

Proofreader

Paul Hindle

Indexer

Hemangini Bari

Graphics

Abhinash Sahu

Production Coordinator

Sushma Redkar

Cover Work

Sushma Redkar

About the Authors

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.

I would like to thank my father, for pushing me to think analytically, and my mother, who taught me that the most interesting thing to think about is people.

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. Since 2005, Data Tactics has been a premier Big Data and analytics service provider based in Washington D.C., serving customers globally.

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.

In addition to co-authoring Social Media Mining in R , Richard has also recently reviewed Making Big Data Work for Your Business for Packt Publishing, and also writes frequently on related topics for the Big Data Republic (http://www.bigdatarepublic.com/bloggers.asp#Rich_Heimann). He has recently assisted DARPA, DHS, the US Army, and the Pentagon with analytical support.

I'd like to thank my mother who has been supportive and still makes every effort to understand and contribute to my thinking.

About the Reviewers

Carlos J. Gil Bellosta is a data scientist who originally trained as a mathematician. He has worked as a freelance statistical consultant for 10 years. Among his many projects, he participated in the development of several natural language processing tools for the Spanish language in Molino de Ideas, a startup based in Madrid. He is currently a senior data scientist at eBay in Zurich.

He is an R enthusiast and has developed several R packages, and is also an active member of the R community in his native Spain. He is one of the founders and the first president of the Comunidad R Hispano, the association of R users in Spain. He has also participated in the organization of the yearly conferences on R in Spain.

Finally, he is an active blogger and writes on statistics, data mining, natural language processing, and all things numerical at http://www.datanalytics.com.

Vibhav Vivek Kamath holds a master's degree in Industrial Engineering and Operations Research from the Indian Institute of Technology, Bombay and a bachelor's degree in Electronics Engineering from the College of Engineering, Pune. During his post-graduation, he was intrigued by algorithms and mathematical modelling, and has been involved in analytics ever since. He is currently based out of Bangalore, and works for an IT services firm. As part of his job, he has developed statistical/mathematical models based on techniques such as optimization and linear regression using the R programming language. He has also spent quite some time handling data visualization and dashboarding for a leading global bank using platforms such as SAS, SQL, and Excel/VBA.

In the past, he has worked on areas such as discrete event simulation and speech processing (both on MATLAB) as part of his academics. He likes building hobby projects in Python and has been involved in robotics in the past. Apart from programming, Vibhav is interested in reading and likes both fiction and non-fiction. He plays table tennis in his free time, follows cricket and tennis, and likes solving puzzles (Sudoku and Kakuro) when really bored. You can get in touch with him at <> with regards to any of the topics above or anything else interesting for that matter!

Feng Mai is currently a PhD candidate in the Department of Operations, Business Analytics, and Information Systems at Carl H. Lindner College of Business, University of Cincinnati. He received a BA in Mathematics from Wabash College and an MS in Statistics from Miami University. He has taught undergraduate business core courses such as business statistics and decision models. His research interests include user-generated content, supply chain analytics, and quality management. His work has been published in journals such as Marketing Science and Quality Management Journal .

Ajay Ohri is the founder of the analytics startup Decisionstats.com. He has pursued graduate studies at the University of Tennessee, Knoxville and the Indian Institute of Management, Lucknow. In addition, Ohri has a mechanical engineering degree from the Delhi College of Engineering. He has interviewed more than 100 practitioners in analytics, including leading members from all the analytics software vendors. Ohri has written almost 1,300 articles on his blog, besides guest writing for influential analytics communities. He teaches courses in R through online education and has worked as an analytics consultant in India for the past decade. Ohri was one of the earliest independent analytics consultants in India, and his current research interests include spreading open source analytics and analyzing social media manipulation, simpler interfaces to cloud computing, and unorthodox cryptography. He is the author of

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