Relationship Between Poetic Meter and Meaning in Accentual-Syllabic Verse (data and replication code)

main.py: script to train both lda and word2vec models

main.ipynb: Jupyter Notebook containing all the analyses reported in the paper

pos.ipynb: clustering based on frequencies of parts-of-speech

corpora: contains original data for Czech, English, and Dutch poetry in JSON (proprietary German and Russian not included)

{ <= Each item in the following lists corresponds to particular poem and holds:
'words': [] <= list of lemmata found in the poem
'pos_tags': [] <= their POS-tags (Positional Morphological Tags for Czech,
MyStem for Russian, TreeTagger tagsets for other corpora)
'meters': [[]] <= list of meters found in poem
'years': [] <= year when poem published (year when author born in case of English)
'n_words': [] <= number of words
'n_lines': [] <= number of lines
'authors': [] <= author of the poem
'titles': [] <= title of the poem
'schemes': [] <= line-ending schemes
}

dicts: contains Gensim dictionary files for all 5 corpora

fig: contains all resulting figures

json > metadata: contains all metadata on poems in particular corpora

{ <= Each item in the following lists corresponds to particular poem and holds:
'meters': [[]] <= list of meters found in poem
'years': [] <= year when poem published (year when author born in case of English)
'n_words': [] <= number of words
'n_lines': [] <= number of lines
'authors': [] <= author of the poem
'titles': [] <= title of the poem
}

json > topics: contains topic probabilities in particular poems

[ <= each item corresponds to particular poem and comprise 100-dimensional dict
{
'topic title': its probability in poem
}
]

json > pos: contains POS relative frequencies in particular poems

[ <= each item corresponds to particular poem
{
'POS': its frequency
}
]

json > w2v: contains mapping of lemmata and their neighbours in word2vec models

models: contains pretrained lda and word2vec models (Gensim)