A Computational Approach to Style in American Poetry
This work addresses the need for computational tools in literary analysis, offering incremental improvements for academic research and personalized poetry recommendations.
The authors tackled the problem of quantifying and visualizing stylistic differences in American poetry by developing a method that analyzes orthographic, syntactic, and phonemic features, showing it outperforms traditional word-occurrence approaches in delineating poetry style.
We develop a quantitative method to assess the style of American poems and to visualize a collection of poems in relation to one another. Qualitative poetry criticism helped guide our development of metrics that analyze various orthographic, syntactic, and phonemic features. These features are used to discover comprehensive stylistic information from a poem's multi-layered latent structure, and to compute distances between poems in this space. Visualizations provide ready access to the analytical components. We demonstrate our method on several collections of poetry, showing that it better delineates poetry style than the traditional word-occurrence features that are used in typical text analysis algorithms. Our method has potential applications to academic research of texts, to research of the intuitive personal response to poetry, and to making recommendations to readers based on their favorite poems.