Social Media Mining With R by Danneman, Nathan -- Read -- Imperial Library of Trantor

Index

Social Media Mining with R

Table of Contents Social Media Mining with R Credits About the Authors About the Reviewers www.PacktPub.com

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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

Scherer's 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