In this chapter, we looked at how to address the massive volume of textual data that exists through text mining methods. We looked at a useful framework for text mining, including preparation, word frequency counts and visualization, and topic models using LDA with the tm
package. Included in this framework were other quantitative techniques such as polarity and formality in order to provide a deeper lexical understanding, or what one could call style, with the qdap
package. The framework was then applied to President Obama's six State of the Union addresses, which showed that although the speeches had a similar style, the core messages changed over time as the political landscape changed. Despite it not being practical to cover every possible text mining technique, those discussed in this chapter should be adequate for most problems that one might face.
Mastering Machine Learning with R
By :
Mastering Machine Learning with R
By:
Overview of this book
Table of Contents (20 chapters)
Mastering Machine Learning with R
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Free Chapter
A Process for Success
Linear Regression – The Blocking and Tackling of Machine Learning
Logistic Regression and Discriminant Analysis
Advanced Feature Selection in Linear Models
More Classification Techniques – K-Nearest Neighbors and Support Vector Machines
Classification and Regression Trees
Neural Networks
Cluster Analysis
Principal Components Analysis
Market Basket Analysis and Recommendation Engines
Time Series and Causality
Text Mining
R Fundamentals
Index
Customer Reviews