Book Image

Social Media Mining with R

By : Richard Heimann, Nathan Danneman
Book Image

Social Media Mining with R

By: Richard Heimann, Nathan Danneman

Overview of this book

<p>The growth of social media over the last decade has revolutionized the way individuals interact and industries conduct business. Individuals produce data at an unprecedented rate by interacting, sharing, and consuming content through social media. However, analyzing this ever-growing pile of data is quite tricky and, if done erroneously, could lead to wrong inferences.</p> <p>By using this essential guide, you will gain hands-on experience with generating insights from social media data. This book provides detailed instructions on how to obtain, process, and analyze a variety of socially-generated data while providing a theoretical background to help you accurately interpret your findings. You will be shown R code and examples of data that can be used as a springboard as you get the chance to undertake your own analyses of business, social, or political data.</p> <p>The book begins by introducing you to the topic of social media data, including its sources and properties. It then explains the basics of R programming in a straightforward, unassuming way. Thereafter, you will be made aware of the inferential dangers associated with social media data and how to avoid them, before describing and implementing a suite of social media mining techniques.</p> <p>Social Media Mining in R provides a light theoretical background, comprehensive instruction, and state-of-the-art techniques, and by reading this book, you will be well equipped to embark on your own analyses of social media data.</p>
Table of Contents (14 chapters)
Social Media Mining with R
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface
Index

Case study 3 – IRT models for unsupervised sentiment scaling


The theoretical underpinnings of IRT models were set out in the previous chapter. Here, we briefly review them before demonstrating how to implement this class of models. However, readers should note that this class of model is cutting-edge, to the point of being considered experimental.

IRT models for text analysis start with the strong assumption that texts (or authors thereof) lie along a continuum, and that this continuum directly affects their word choices in a monotonic way such that if word use is likely at one end of the spectrum, it is unlikely at the other. These assumptions are somewhat restrictive; we can only scale texts that deal with moderately narrow topics and that are subject to word choice differences. Furthermore, it is important that the sentiment continuum be that underlying continuum; else, the model will estimate whatever continuum underlies the data. A good example would be the debate about the Affordable...