Orlando Furioso: A Computational Analysis of Gender Bias

This thesis examines gender bias in Orlando furioso using computational methods integrated with literary analysis. Gender is approached as a discursive pattern emerging from linguistic attribution rather than a fixed category. Using contextual embeddings, distributional association measures, and syntactic parsing, the study analyzes characters and adjectives in the 1532 edition of the poem. The results reveal heterogeneous and context-dependent gendered associations, highlighting the poem’s resistance to ideological stabilization and demonstrating the value of computational approaches as exploratory tools in literary studies.