English Text Classification by Authorship and Date

We performed two experiments with statistical techniques for classifying documents by date and author, using large bodies of publicly-available texts. In one experiment, we produced a Markov chain of every United States Supreme Court opinion ever written, and evaluated its ability to classify American judicial opinions by decade of authorship. In the other, we examined the performance of two sets of quasi-linguistic features in classifying op-ed articles from The New York Times among four authors with a supportvector machine. The results in each case were encouraging. With the Markov chain, we could correctly identify the decade of authorship of a Supreme Court opinion within one decade 85 percent of the time. With the two quasi-linguistic feature sets, we were able to measure the equivocation between pairs of authors and observe some interesting effects when more features were collected.