帮楼主贴一下此书的TOC，方便广大感兴趣的坛友，:-)
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 R Why 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 sentiment
Supervised 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
Packt.Social Media Mining with R.2014.pdf
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