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Finding contrasting patterns in rhythmic properties between prose and poetry

Author

Listed:
  • Ferraz de Arruda, Henrique
  • Reia, Sandro Martinelli
  • Silva, Filipi Nascimento
  • Amancio, Diego Raphael
  • da Fontoura Costa, Luciano

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.

Suggested Citation

  • Ferraz de Arruda, Henrique & Reia, Sandro Martinelli & Silva, Filipi Nascimento & Amancio, Diego Raphael & da Fontoura Costa, Luciano, 2022. "Finding contrasting patterns in rhythmic properties between prose and poetry," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 598(C).
    • Handle: RePEc:eee:phsmap:v:598:y:2022:i:c:s0378437122002965
    DOI: 10.1016/j.physa.2022.127387
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    References listed on IDEAS

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    1. Silva, Filipi N. & Amancio, Diego R. & Bardosova, Maria & Costa, Luciano da F. & Oliveira, Osvaldo N., 2016. "Using network science and text analytics to produce surveys in a scientific topic," Journal of Informetrics, Elsevier, vol. 10(2), pages 487-502.
    2. Diego Raphael Amancio & Cesar Henrique Comin & Dalcimar Casanova & Gonzalo Travieso & Odemir Martinez Bruno & Francisco Aparecido Rodrigues & Luciano da Fontoura Costa, 2014. "A Systematic Comparison of Supervised Classifiers," PLOS ONE, Public Library of Science, vol. 9(4), pages 1-14, April.
    3. Diego Raphael Amancio, 2015. "A Complex Network Approach to Stylometry," PLOS ONE, Public Library of Science, vol. 10(8), pages 1-21, August.
    4. Diego R Amancio, 2015. "Probing the Topological Properties of Complex Networks Modeling Short Written Texts," PLOS ONE, Public Library of Science, vol. 10(2), pages 1-17, February.
    5. Massimo Stella, 2018. "Cohort and Rhyme Priming Emerge from the Multiplex Network Structure of the Mental Lexicon," Complexity, Hindawi, vol. 2018, pages 1-14, September.
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