In this chapter, we discussed how to deal with text in R in order to perform classification. We examined how to load documents from several sources, preprocess them, and how to compute term frequencies. We compared the reliability of various algorithms in the classification such as Naïve Bayes, k-Nearest Neighbors, logistic regression, and support vector machines. Additionally, we examined how to perform basic topic modeling in order to extract meaning. We then studied how to automatically download news articles from sources such as The New York Times Article Search API and extract and visualize associations between terms.
In the next chapter, we will discuss cross-validation and how to export models using the PMML.
