Social media mining with R: deploy cutting-edge sentiment analysis techniques to real-world social media data using R

✍ Scribed by Danneman, Nathan;Heimann, Richard;Wood, Monseé G

• Publisher: Packt Publishing
• Year: 2014
• Tongue: English
• Series: Community experience distilled
• Category: Library

✦ Synopsis

A concise, hands-on guide with many practical examples and a detailed treatise on inference and social science research that will help you in mining data in the real world. Whether you are an undergraduate who wishes to get hands-on experience working with social data from the Web, a practitioner wishing to expand your competencies and learn unsupervised sentiment analysis, or you are simply interested in social data analysis, this book will prove to be an essential asset. No previous experience with R or statistics is required, though having knowledge of both will enrich your experience.;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

✦ Table of Contents

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

✦ Subjects

COMPUTERS--Data Modeling & Design;COMPUTERS--Web--Social Networking;COMPUTERS--Web--User Generated Content;Data mining;Social media;Electronic books;COMPUTERS -- Web -- User Generated Content;COMPUTERS -- Web -- Social Networking;COMPUTERS -- Data Modeling & Design