Una Comprensione Computazionale della Psiche Emotiva e Ordine nelle Ballate del Decameron: Stilometria e Elaborazione del Linguaggio Naturale
Year of Graduation
Level of Access
Open Access Thesis
Department or Program
Romance Languages and Literatures
The present thesis, written in Italian, explores the emotional psyche and narrative order embedded within the ballads of the Decameron, a renowned literary masterpiece by Giovanni Boccaccio. Leveraging the advancements in stylometry and natural language processing techniques, this research aims to convince medieval Italian literature scholars to produce more on scholarship of the ballads and uncover the intricate patterns of human emotions and narrative organization in the ballads. The study begins by establishing a comprehensive corpus of ballads from the Decameron, utilizing digital libraries and text repositories. Subsequently, using stylometric analysis, the research examines the linguistic and stylistic features that distinguish the brigata’s ballads, focusing on elements such as vocabulary, syntax, and rhyme scheme. These analyses enable the identification of authorial patterns, shedding light on the emotional expressions and narrative techniques employed by Boccaccio. A natural language processing model was used to predict authorship of the ballads using each of the brigata’s novelle as training data. The findings of this research contribute to a deeper understanding of the emotional and narrative purpose of the Decameron's ballads. Results of stylometric analysis allowed for new characterization of Panfilo’s ballad as sad and revealed how similarity in the emotional psyches of the brigata transcends gender. These novel perspectives allowed for unique literary analysis of the ballads. Accurate prediction of ballad authorship demonstrates that ballads fit into the narrative structure of the Decameron and restore order in each of the ten days.
European Languages and Societies Commons, Italian Linguistics Commons, Italian Literature Commons, Medieval Studies Commons