Modeling Gendered Semantic Differences in English-Language Poetry

Abstract

This thesis presents a computational exploration of gendered semantic differences in English-language poetry. By training separate Word2Vec and FastText embedding models on collections of male- and female-authored poems across multiple time periods, we investigate the way poets of different genders use language in quantitatively distinct ways. Nearest-neighbor embedding visualizations and n-gram-based co-occurrence networks were used to identify meaningful semantic relationships between words, which often reflected broader sociocultural narratives around gender. The findings suggest that while semantic distinctions between male and female poets have become less pronounced in more contemporary periods, they remain detectable, especially around themes of identity, embodiment, and domesticity. Co- occurrence networks further illustrate thematic clustering and distinct community structures that vary across gender and era. While computational tools cannot fully capture the metaphorical ambiguity or emotional content of poetry, they offer new modes of inquiry, enabling the analysis of linguistic patterns that poetry has long conveyed through style and form. This work contributes to the growing field of computational text analysis, demonstrating that even through quantitative frameworks, language continues to carry the nuance of human thought and experience.