In this chapter, we looked at the text mining-based problem of authorship attribution. To perform this, we analyzed two types of features: function words and character n-grams. For function words, we were able to use the bag-of-words model—simply restricted to a set of words we chose beforehand. This gave us the frequencies of only those words. For character n-grams, we used a very similar workflow using the same class. However, we changed the analyzer to look at characters and not words. In addition, we used n-grams that are sequences of n tokens in a row—in our case characters. Word n-grams are also worth testing in some applications, as they can provide a cheap way to get the context of how a word is used.
For classification, we used SVMs that optimize a line of separation between the classes based on the idea of finding the maximum margin. Anything above the line is one class and anything...
