A Computational Approach to Style in American Poetry (original) (raw)
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A computational analysis of poetic style
Linguistic Issues in Language Technology, 2015
How do standards of poetic beauty change as a function of time and expertise? Here we use computational methods to compare the stylistic features of 359 English poems written by 19th century professional poets, Imagist poets, contemporary professional poets, and contemporary amateur poets. Building upon techniques designed to analyze style and sentiment in texts, we examine elements of poetic craft such as imagery, sound devices, emotive language, and diction. We find that contemporary professional poets use significantly more concrete words than 19th century poets, fewer emotional words, and more complex sound devices. These changes are consistent with the tenets of Imagism, an early 20thcentury literary movement. Further analyses show that contemporary amateur poems resemble 19th century professional poems more than contemporary professional poems on several dimensions. The stylistic similarities between contemporary amateur poems and 19th century professional poems suggest that e...
2013
We present SPARSAR, a system for the automatic analysis of poetry(and text) style which makes use of NLP tools like tokenizers, sentence splitters, NER (Name Entity Recognition) tools, and taggers. Our system in addition to the tools listed above which aim at obtaining the same results of quantitative linguistics, adds a number of additional tools for syntactic and semantic structural analysis and prosodic modeling. We use a constituency parser to measure the structure of modifiers in NPs; and a dependency mapping of the previous parse to analyse the verbal complex and determine Polarity and Factuality. Another important component of the system is a phonological parser to account for OOVWs, in the process of grapheme to phoneme conversion of the poem. We also measure the prosody of the poem by associating mean durational values in msecs to each syllable from a database and created an algorithm to account for the evaluation of durational values for any possible syllable structure. Ev...
A Computational Analysis of Style, Affect, and Imagery in Contemporary Poetry
2012
What makes a poem beautiful? We use computational methods to compare the stylistic and content features employed by awardwinning poets and amateur poets. Building upon existing techniques designed to quantitatively analyze style and affect in texts, we examined elements of poetic craft such as diction, sound devices, emotive language, and imagery. Results showed that the most important indicator of high-quality poetry we could detect was the frequency of references to concrete objects. This result highlights the influence of Imagism in contemporary professional poetry, and suggests that concreteness may be one of the most appealing features of poetry to the modern aesthetic. We also report on other features that characterize high-quality poetry and argue that methods from computational linguistics may provide important insights into the analysis of beauty in verbal art.
A Computational Approach to Poetic Structure, Rhythm and Rhyme
2014
English. In this paper we present SPARSAR, a system for the automatic analysis of English and Italian poetry. The system can work on any type of poem and produces a set of parameters that are then used to compare poems with one another, of the same author or of different authors. In this paper, we will concentrate on the second module, which is a rule-based system to represent and analyze poetic devices. Evaluation of the system on the basis of a manually created dataset including poets from Shakespeare's time down to T.S.Eliot and Sylvia Plath has shown its high precision and accuracy approximating 90%. Italiano. In questo lavoro presentiamo SPARSAR, un sistema per l'analisi automatica di poesia inglese e italiana. Il sistema e in grado di lavorare su qualunque poesia e produce un insieme di parametri che vengono poi usati per confrontare poesie e autori tra di loro. In questo lavoro ci concentreremo sul secondo modulo che consiste in un sistema a regole per rappresentare e...
Characterizing English poetic style using complex networks
International Journal of …, 2012
Complex networks have been proven useful in characterizing written texts. Here, we use networks to probe if there exist a similarity within, and difference across, era as reflected within the poem's structure. In literary history, boundary lines are set to distinguish the change in writing styles through time. We obtain the network parameters and motif frequencies of 845 poems published from 1522 to 1931 and relate this to the writing of the Elizabethan, 17th Century, Augustan, Romantic and Victorian eras. Analysis of the different network parameters shows a significant difference of the Augustan era (1667-1780) with the rest. The network parameters and the convex hull and centroids of the motif frequencies reflect the adjectival sequence pattern of the poems of the Augustan era.
2013
We present SPARSAR, a system for the automatic analysis of poetry(and text) style which makes use of NLP tools like tokenizers, sentence splitters, NER (Name Entity Recognition) tools, and taggers. Our system in addition to the tools listed above which aim at obtaining the same results of quantitative linguistics, adds a number of additional tools for syntactic and semantic structural analysis and prosodic modeling. We use a constituency parser to measure the structure of modifiers in NPs; and a dependency mapping of the previous parse to analyse the verbal complex and determine Polarity and Factuality. Another important component of the system is a phonological parser to account for OOVWs, in the process of grapheme to phoneme conversion of the poem. We also measure the prosody of the poem by associating mean durational values in msecs to each syllable from a database and created an algorithm to account for the evaluation of durational values for any possible syllable structure. Ev...
Predicting and Classifying the Poetic Quality of the Early and the Late Generation of Romantics: A Computational Stylistic Study, 2023
Poetic quality is an important value for a poet’s literary craft, and it is considered as a cultural heritage of a society. Poetry is made with language and poets use language features in a particular way to create effects and to convey meaning. Measuring poetic quality would appear to be an intrinsically qualitative endeavor. However, it is possible to objectify some of the language features to some degree using a quantitative criterion, which is here proposed. The study represents a preliminary literary computational approach in relation to Romantic poetry which is dominated by a few names: William Blake, William Wordsworth, Samuel Taylor Coleridge, Percy Bysshe Shelley, John Keats, and Lord Byron. There has been little or no research with literary computing techniques focused on measuring and classifying the quality levels of those poets’ works. The study builds on a set of ten measurable stylistic features extracted from one hundred and eight poems regardless of genre or subject. The study carries out an analysis using Binary Logistic Regression Modeling (BLRM) as one of the most commonly used predictive modeling techniques. I used the stylistic features as predictive variables and the quality as the outcome variable indicative of the stylistic characteristics of each of the poems selected. In this way, this study is able to measure the stylistic features in the selected poems and, independently, using Binary Logistic Regression to predict the general poetic quality levels for each of the poets examined. The study is also able to make connections between some of the poets which enabled a more detailed view of the subcomponents’ usage and occurrence in the poems.
Large-scale Analysis of Spoken Free-verse Poetry
Most modern and post-modern poems have developed a post-metrical idea of lyrical prosody that employs rhythmical features of everyday language and prose instead of a strict adherence to rhyme and metrical schemes. This development is subsumed under the term free verse prosody. We present our methodology for the large-scale analysis of modern and post-modern poetry in both their written form and as spoken aloud by the author. We employ language processing tools to align text and speech, to generate a null-model of how the poem would be spoken by a na¨ıve reader, and to extract contrastive prosodic features used by the poet. On these, we intend to build our model of free verse prosody, which will help to understand, differentiate and relate the different styles of free verse poetry. We plan to use our processing scheme on large amounts of data to iteratively build models of styles, to validate and guide manual style annotation, to identify further rhythmical categories, and ultimately to broaden our understanding of free verse poetry. In this paper, we report on a proof-of-concept of our methodology using smaller amounts of poems and a limited set of features. We find that our methodology helps to extract differentiating features in the authors' speech that can be explained by philological insight. Thus, our automatic method helps to guide the literary analysis and this in turn helps to improve our computational models.