This chapter's required packages

Setting up the packages for this chapter may be a bit cumbersome because some of the packages depend on operating system libraries which can vary from computer to computer. Please check Appendix, Required Packages for specific instructions on how to install them for your operating system.

Package	Reason
lsa	Cosine similarity computation
rilba	Efficient SVD decomposition
caret	Machine learning framework
twitteR	Interface to Twitter's API
quanteda	Text data processing
sentimentr	Text data sentiment analysis
randomForest	Random forest models

We will use the rilba package (which depends on C code) to compute a part of the Singular Value Decomposition (SVD) efficiently using the Augmented Implicitly Restarted Lanczos Bidiagonalization Methods, by Baglama and Reichel, 2005, http://www.math.uri.edu/~jbaglama/papers...