Abstract
Poetry and prose are written artistic expressions that help us appreciate the reality we live in. Each of these styles has its own set of subjective properties, such as rhyme and rhythm, which are easily caught by a human reader’s eye and ear. With the recent advances in artificial intelligence, the gap between humans and machines may have decreased, and today we observe algorithms mastering tasks that were once exclusively performed by humans. In this paper, we propose a computational method to distinguish between poetry and prose based solely on aural and rhythmic properties. In order to compare prose and poetry rhythms, we represent the rhymes and phonemes as temporal sequences, and thus, we propose a procedure for extracting rhythmic features from these sequences. The performance of this procedure is evaluated by the use of popular machine learning classifiers, and the best accuracy was obtained with a multilayer perceptron neural network. Interestingly, by using an approach based on complex networks to visualize the similarities between the different texts considered, we found that the patterns of poetry vary more than prose. Consequently, a richer and more complex set of rhythmic possibilities tends to be found in that modality.
