The Enron application we used ended up using just a portion of the overall dataset. There is lots more data available in this dataset. Increasing the number of authors will likely lead to a drop in accuracy, but it is possible to boost the accuracy further than was achieved in this chapter, using similar methods. Using a Grid Search, try different values for n-grams and different parameters for support vector machines, in order to get better performance on a larger number of authors.

The dataset used in Chapter 12, Working with Big Data, provides authorship-based classes (each blogger ID is a separate author). This dataset can be tested using this kind of method as well. In addition, there are the other classes of gender, age, industry, and star sign that can be tested—are authorship-based methods good for these classification tasks?

https://github.com/robertlayton/authorship_tutorials/blob/master/LNGTutorial.ipynb

Another form of classifier is local n-gram, which involves choosing the best features per-author, not globally for the entire dataset. I wrote a tutorial on using local n-grams for authorship attribution, available at the above link.