@inproceedings{picca-2024-fluid,
    title = "Fluid Dynamics-Inspired Emotional Analysis in {S}hakespearean Tragedies: A Novel Computational Linguistics Methodology",
    author = "Picca, Davide",
    editor = "Valentino, Marco  and
      Ferreira, Deborah  and
      Thayaparan, Mokanarangan  and
      Freitas, Andre",
    booktitle = "Proceedings of the 2nd Workshop on Mathematical Natural Language Processing @ LREC-COLING 2024",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.mathnlp-1.2",
    pages = "11--18",
    abstract = "This study introduces an innovative method for analyzing emotions in texts, drawing inspiration from the principles of fluid dynamics, particularly the Navier-Stokes equations. It applies this framework to analyze Shakespeare{'}s tragedies {``}Hamlet{''} and {``}Romeo and Juliet{''}, treating emotional expressions as entities akin to fluids. By mapping linguistic characteristics onto fluid dynamics components, this approach provides a dynamic perspective on how emotions are expressed and evolve in narrative texts. The results, when compared with conventional sentiment analysis methods, reveal a more detailed and subtle grasp of the emotional arcs within these works. This interdisciplinary strategy not only enriches emotion analysis in computational linguistics but also paves the way for potential integrations with machine learning in NLP.",
}

This study introduces an innovative method for analyzing emotions in texts, drawing inspiration from the principles of fluid dynamics, particularly the Navier-Stokes equations. It applies this framework to analyze Shakespeare{'}s tragedies {``}Hamlet{''} and {``}Romeo and Juliet{''}, treating emotional expressions as entities akin to fluids. By mapping linguistic characteristics onto fluid dynamics components, this approach provides a dynamic perspective on how emotions are expressed and evolve in narrative texts. The results, when compared with conventional sentiment analysis methods, reveal a more detailed and subtle grasp of the emotional arcs within these works. This interdisciplinary strategy not only enriches emotion analysis in computational linguistics but also paves the way for potential integrations with machine learning in NLP.
Picca, Davide
Fluid Dynamics-Inspired Emotional Analysis in Shakespearean Tragedies: A Novel Computational Linguistics Methodology