Pull out all the stops: Textual analysis via punctuation sequences

descriptionPublicationkeyboard_double_arrow_right Article , Preprint , Other literature type 01 Jan 2019Embargo end date: 01 Jan 2019 United Kingdom Publisher:Center for Open ScienceJournal:European Journal of Applied Mathematics, volume 32, pages 1,069-1,105 (issn: 0956-7925, eissn: 1469-4425,

Copyright policy )Funded by:UKRI | The Alan Turing Institute

Authors: Darmon, ANM; Bazzi, M; Howison, S; Porter, MA;

doi: 10.31235/osf.io/2rzsg , 10.1017/s0956792520000157 , 10.5281/zenodo.3605100 , 10.48550/arxiv.1901.00519 , 10.5281/zenodo.3605099

arXiv: 1901.00519

Pull out all the stops: Textual analysis via punctuation sequences

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Abstract

Whether enjoying the lucid prose of a favorite author or slogging through some other writer's cumbersome, heavy-set prattle (full of parentheses, em-dashes, compound adjectives, and Oxford commas), readers will notice stylistic signatures not only in word choice and grammar, but also in punctuation itself. Indeed, visual sequences of punctuation from different authors produce marvelously different (and visually striking) sequences. Punctuation is a largely overlooked stylistic feature in ``stylometry'', the quantitative analysis of written text. In this paper, we examine punctuation sequences in a corpus of literary documents and ask the following questions: Are the properties of such sequences a distinctive feature of different authors? Is it possible to distinguish literary genres based on their punctuation sequences? Do the punctuation styles of authors evolve over time? Are we on to something interesting in trying to do stylometry without words, or are we full of sound and fury (signifying nothing)?

Country

United Kingdom

Related Organizations

University of Warwick
United Kingdom
University of California
United States
University of Oxford
United Kingdom
University of California, San Francisco
United States
The Alan Turing Institute

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Keywords

FOS: Computer and information sciences, Computer Science - Machine Learning, Physics - Physics and Society, SocArXiv|Social and Behavioral Sciences|Linguistics, FOS: Physical sciences, Physics and Society (physics.soc-ph), Social and Behavioral Sciences, QA76, Machine Learning (cs.LG), computational linguistics, categorical time series, computational methods, Mathematics and literature, mathematical modelling, natural language processing, QA, bepress|Social and Behavioral Sciences|Linguistics, Computer Science - Computation and Language, Natural language processing, Markov processes, Pattern recognition, speech recognition, SocArXiv|Arts and Humanities, Linguistics, P1, bepress|Social and Behavioral Sciences|Linguistics|Computational Linguistics, Computational Linguistics, stylometry, bepress|Social and Behavioral Sciences, SocArXiv|Social and Behavioral Sciences|Linguistics|Computational Linguistics, SocArXiv|Social and Behavioral Sciences, Arts and Humanities, digital humanities, Computation and Language (cs.CL), bepress|Arts and Humanities

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