Social Media Mining with R

Cover
 Copyright
 Credits
 About the Authors
 About the Reviewers
 www.PacktPub.com
 Table of Contents
 Preface
 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
 Summary
 Chapter 2: Getting Started with R
 Why R?
 Quick start
 The basics --
assignment and arithmetic
 Functions, arguments, and help
 Vectors, sequences, and combining vectors
 A quick example --
creating data frames and importing files
 Visualization in R
 Style and workflow
 Additional resources
 Summary Chapter 3: Mining Twitter with RWhy Twitter data?
 Obtaining Twitter data
 Preliminary analyses
 Summary
 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
 Summary
 Chapter 5: Social Media Mining --
Fundamentals
 Key concepts of social media mining
 Good data versus bad data
 Understanding sentiments
 Scherer\'s typology of emotions
 Sentiment polarity --
data and classification Supervised social media mining --
lexicon-based sentimentSupervised social media mining --
Naive Bayes classifiers
 Unsupervised social media mining --
Item Response Theory for text scaling
 Summary
 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
 Summary
 Appendix: Conclusions and Next Steps
 Final thoughts
 An expanding field
 Further reading
 Bibliography
 Index