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Social Media Mining with R

Richard Heimann, Nathan Danneman

There’s probably no better place to gain behavioral insights than through social media, but analyzing the mass of data is often difficult. With this book you’ll learn to employ the latest techniques and processes using R.
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Book Details

ISBN 139781783281770
Paperback122 pages

About 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.

Table of Contents

Chapter 1: Going Viral
Social media mining using sentiment analysis
The state of communication
What is Big Data?
Human sensors and honest signals
Quantitative approaches
Chapter 2: Getting Started with R
Why R?
Quick start
Vectors, sequences, and combining vectors
A quick example – creating data frames and importing files
Visualization in R
Style and workflow
Additional resources
Chapter 3: Mining Twitter with R
Why Twitter data?
Obtaining Twitter data
Preliminary analyses
Chapter 4: Potentials and Pitfalls of Social Media Data
Opinion mining made difficult
Sentiment and its measurement
The nature of social media data
Traditional versus nontraditional social data
Measurement and inferential challenges
Chapter 5: Social Media Mining – Fundamentals
Key concepts of social media mining
Good data versus bad data
Understanding sentiments
Sentiment polarity – data and classification
Supervised social media mining – lexicon-based sentiment
Supervised social media mining – Naive Bayes classifiers
Unsupervised social media mining – Item Response Theory for text scaling
Chapter 6: Social Media Mining – Case Studies
Introductory considerations
Case study 1 – supervised social media mining – lexicon-based sentiment
Case study 2 – Naive Bayes classifier
Case study 3 – IRT models for unsupervised sentiment scaling

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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