Interdisciplinary Stylometric Analysis of Shakespeare’s Translations: Unveiling Gender Dynamics through Lexical, Sentential, and Metrical Layers

While Shakespeare’s works have been extensively analyzed, the influence of translator gender on stylistic features remains underexplored, and most certainly not via computational linguistics. This study aims to address this gap by investigating the intersection of gender and translation in the stylistic choices made by translators of Shakespearean plays into three different languages, addressing a notable gap in quantitative research within Translation Studies. The research uses advanced computational methods such as Python’s sklearn library for Principal Component Analysis and hierarchical clustering to analyze the lexical, syntactic, and metrical elements involved in the translation process, focusing on data processing, tokenization, and stemming from studying the impact of gender on translation patterns. The findings reveal that translations cluster distinctly by language but exhibit significant stylistic differences based on the translator’s gender. Female translators use more diverse words and sentence patterns, which shows they favor creative language and detailed interpretation of original texts. Male translators display stronger fidelity to Shakespeare’s iambic pentameter in their Spanish sonnet translations. The research reveals that translators’ gender choices play a role in selecting words and understanding and presenting the original content. These findings show how translators’ gender backgrounds inform their translation choices, giving us fresh ways to understand old texts amid modern views on gender.