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Spis treści

Social Media Mining with R eBook -- spis treści

• Social Media Mining with R
  • Table of Contents
  • Social Media Mining with R
  • Credits
  • About the Authors
  • About the Reviewers
  • www.PacktPub.com
    • Support files, eBooks, discount offers and more
      • Why Subscribe?
      • Free Access for Packt account holders
  • Preface
    • What this book covers
    • What you need for this book
    • Who this book is for
    • Conventions
    • Reader feedback
    • Customer support
      • Downloading the example code
      • Downloading the color images of this book
      • Errata
      • Piracy
      • Questions
  • 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
  • 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
  • 3. Mining Twitter with R
    • Why Twitter data?
    • Obtaining Twitter data
    • Preliminary analyses
    • Summary
  • 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
  • 5. Social Media Mining Fundamentals
    • Key concepts of social media mining
    • Good data versus bad data
    • Understanding sentiments
      • Scherers typology of emotions
    • 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
    • Summary
  • 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
  • A. Conclusions and Next Steps
    • Final thoughts
    • An expanding field
    • Further reading
    • Bibliography
  • Index