IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v598y2022ics0378437122002965.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437122002965
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2022.127387?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Diego Raphael Amancio, 2015. "A Complex Network Approach to Stylometry," PLOS ONE, Public Library of Science, vol. 10(8), pages 1-21, August.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jorge A. V. Tohalino & Laura V. C. Quispe & Diego R. Amancio, 2021. "Analyzing the relationship between text features and grants productivity," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(5), pages 4255-4275, May.
    2. de Arruda, Henrique F. & Marinho, Vanessa Q. & Lima, Thales S. & Amancio, Diego R. & Costa, Luciano da F., 2018. "An image analysis approach to text analytics based on complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 110-120.
    3. Adilson Vital & Diego R. Amancio, 2022. "A comparative analysis of local similarity metrics and machine learning approaches: application to link prediction in author citation networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(10), pages 6011-6028, October.
    4. Tohalino, Jorge A.V. & Amancio, Diego R., 2022. "On predicting research grants productivity via machine learning," Journal of Informetrics, Elsevier, vol. 16(2).
    5. Tohalino, Jorge V. & Amancio, Diego R., 2018. "Extractive multi-document summarization using multilayer networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 526-539.
    6. Diego Raphael Amancio, 2015. "Comparing the topological properties of real and artificially generated scientific manuscripts," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(3), pages 1763-1779, December.
    7. Samuel Zanferdini Oliva & Livia Oliveira-Ciabati & Denise Gazotto Dezembro & Mário Sérgio Adolfi Júnior & Maísa Carvalho Silva & Hugo Cesar Pessotti & Juliana Tarossi Pollettini, 2021. "Text structuring methods based on complex network: a systematic review," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(2), pages 1471-1493, February.
    8. Jung, Sukhwan & Yoon, Wan Chul, 2020. "An alternative topic model based on Common Interest Authors for topic evolution analysis," Journal of Informetrics, Elsevier, vol. 14(3).
    9. Corrêa, Edilson A. & Marinho, Vanessa Q. & Amancio, Diego R., 2020. "Semantic flow in language networks discriminates texts by genre and publication date," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 557(C).
    10. Dejian Yu & Wanru Wang & Shuai Zhang & Wenyu Zhang & Rongyu Liu, 2017. "Hybrid self-optimized clustering model based on citation links and textual features to detect research topics," PLOS ONE, Public Library of Science, vol. 12(10), pages 1-21, October.
    11. de Arruda, Henrique F. & Silva, Filipi N. & Comin, Cesar H. & Amancio, Diego R. & Costa, Luciano da F., 2019. "Connecting network science and information theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 515(C), pages 641-648.
    12. Nguyen Minh Tien & Cyril Labbé, 2018. "Detecting automatically generated sentences with grammatical structure similarity," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(2), pages 1247-1271, August.
    13. Corrêa, Edilson A. & Amancio, Diego R., 2019. "Word sense induction using word embeddings and community detection in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 180-190.
    14. 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.
    15. Mariane Barros Neiva & Patrick Guidotti & Odemir Martinez Bruno, 2018. "Enhancing LBP by preprocessing via anisotropic diffusion," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 29(08), pages 1-29, August.
    16. Corrêa Jr., Edilson A. & Silva, Filipi N. & da F. Costa, Luciano & Amancio, Diego R., 2017. "Patterns of authors contribution in scientific manuscripts," Journal of Informetrics, Elsevier, vol. 11(2), pages 498-510.
    17. Wang, Haiying & Wang, Jun & Small, Michael & Moore, Jack Murdoch, 2019. "Review mechanism promotes knowledge transmission in complex networks," Applied Mathematics and Computation, Elsevier, vol. 340(C), pages 113-125.
    18. Pradhan, Dinesh K. & Chakraborty, Joyita & Choudhary, Prasenjit & Nandi, Subrata, 2020. "An automated conflict of interest based greedy approach for conference paper assignment system," Journal of Informetrics, Elsevier, vol. 14(2).
    19. Benatti, Alexandre & Ferraz de Arrruda, Henrique & Nascimento Silva, Filipi & da Fontoura Costa, Luciano, 2021. "Enriching and analyzing small citation networks: A case study on transistor’s history," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 573(C).
    20. Diego R. Amancio & Osvaldo N. Oliveira jr & Luciano F. Costa, 2015. "Topological-collaborative approach for disambiguating authors’ names in collaborative networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(1), pages 465-485, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:598:y:2022:i:c:s0378437122002965. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.