Explorations in an English Poetry Corpus: A Neurocognitive Poetics Perspective
This work provides a curated dataset and analytical methods for researchers in Digital Humanities, Natural Language Processing, or Neurocognitive Poetics, but it is incremental as it applies existing techniques to a new corpus.
The paper tackles the analysis of English poetry by creating the Gutenberg English Poetry Corpus (GEPC) with over 100 poetic texts and 2 million words, and uses Quantitative Narrative Analysis (QNA) to explore author similarities and text metrics, such as lexical diversity and sentiment analysis, for works like George Eliot's 'How Lisa Loved the King' and James Joyce's 'Chamber Music'.
This paper describes a corpus of about 3000 English literary texts with about 250 million words extracted from the Gutenberg project that span a range of genres from both fiction and non-fiction written by more than 130 authors (e.g., Darwin, Dickens, Shakespeare). Quantitative Narrative Analysis (QNA) is used to explore a cleaned subcorpus, the Gutenberg English Poetry Corpus (GEPC) which comprises over 100 poetic texts with around 2 million words from about 50 authors (e.g., Keats, Joyce, Wordsworth). Some exemplary QNA studies show author similarities based on latent semantic analysis, significant topics for each author or various text-analytic metrics for George Eliot's poem 'How Lisa Loved the King' and James Joyce's 'Chamber Music', concerning e.g. lexical diversity or sentiment analysis. The GEPC is particularly suited for research in Digital Humanities, Natural Language Processing or Neurocognitive Poetics, e.g. as training and test corpus, or for stimulus development and control.