PacktLib: Social Media Mining with R

Social Media Mining with R

Credits

About the Authors

About the Reviewers

www.PacktPub.com

Preface

Going Viral

Social media mining using sentiment analysis

The state of communication

What is Big Data?

Human sensors and honest signals

Quantitative approaches

Summary

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

Summary

Mining Twitter with R

Why Twitter data?

Obtaining Twitter data

Preliminary analyses

Summary

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

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

Summary

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

Conclusions and Next Steps

Conclusions and Next Steps

Conclusions and Next Steps

Conclusions and Next Steps

Conclusions and Next Steps

Index