Confirming the themes and interpretive unity of Ghazal poetry using topic models

We apply topic modeling to classifying the genre of Ghazal, a form common in Persian poetry. We show that a classifier based on automatically-generated topics exposes important information with only a small performance penalty: the top dis-criminative topics can be manually aligned with themes prevalent in the associated genres, as identified by scholars of literature. We also weigh in on a long-standing debate about the interpretive unity of Ghazal. In particular, we show evidence that, on the average, Ghazals seem to have interpretive unity at the level of the full poem, as opposed to just at the level of the couplet. Our dataset is a collection of almost 18,000 Ghazal, comprising over 3 million words. The collection contains poems from 30 different poets, and spans nearly 900 years (1080 A.D.–1968). Generative models tend to perform worse than discriminative models in classification tasks. The hope is, however, that a generative model provides additional insight, by allowing decisions to be understood with reference to the generated explanation. We show that this hope holds true for topic models [1] in two specific cases in the domain of Ghazal, a form common in Persian poetry. First