[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-156969-105":59,"doc-detail-156969-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","a-decomposition-of-book-structure-through-ousiometric-fluctuations-in-cumulative-word-time","A decomposition of book structure through ousiometric fluctuations in cumulative word-time","","Quantitative literary analysis often treats word usage as aggregate statistics or studies only narrative-shape trends independent of length. This work introduces cumulative word-time as a length-relative measure to track how words evolve throughout a book. Using an ousiometric reinterpretation of valence-arousal-dominance, the method transforms text into power and danger time series, then decomposes them via empirical mode decomposition into oscillatory modes plus a non-oscillatory trend. Results show shorter books mainly follow a general trend, while longer books add length-robust fluctuations tied to content structure, motivating a data-driven denoising approach and avenues for measuring narrative units.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/a-decomposition-of-book-structure-through-ousiometric-fluctuations-in-cumulative-word-time/156969/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/a-decomposition-of-book-structure-through-ousiometric-fluctuations-in-cumulative-word-time/156969.png","ImageObject",300,407,{"name":92,"@type":93},"Rizky","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-02","2026-08-29",true,{"@type":102,"interactionType":103,"userInteractionCount":44},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What does the paper mean by cumulative word-time?","Question",{"text":112,"@type":113},"Cumulative word-time indexes positions in a book by the number of words encountered, enabling analysis of how language changes over the course of the text independent of simple completion fraction.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How are power and danger time series derived from text?",{"text":117,"@type":113},"The method applies an ousiometric reinterpretation of the valence-arousal-dominance meaning framework and converts text into power and danger scores over time.",{"name":119,"@type":110,"acceptedAnswer":120},"What key difference is found between shorter and longer books?",{"text":121,"@type":113},"Shorter books show mainly a general non-oscillatory trend, whereas longer books exhibit additional oscillatory fluctuations whose periods are typically a few thousand words and depend on content structure.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},156969,1787970591,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":44,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":129,"read_time":144,"preview_page_count":4},962085564807,"https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8","arXiv :2208 .09496v4 [ cs .CL] 12 May 2023  \nA decomposition of book structure through ousiometric 􀀍uctuations in cumulative  \nword-time  \nMikaela Irene Fudolig, 1, 􀀃 Thayer Alshaabi,2, 1 Kathryn Cramer, 1  \nChristopher M. Danforth, 1, 3 and Peter Sheridan Dodds 1, 4  \n1 Computational Story Lab, Vermont Complex Systems Center,  \nMassMutual Center of Excellence for Complex Systems and Data Science,  \nVermont Advanced Computing Core, University of Vermont, Burlington, VT, USA  \n2 Advanced Bioimaging Center, UC Berkeley, Berkeley, CA, USA  \n3 Department of Mathematics & Statistics, University of Vermont, Burlington, VT, USA  \n4 Department of Computer Science, University of Vermont, Burlington, VT, USA  \nWhile quantitative methods have been used to examine changes in word usage in books, studies have focused on overall trends, such as the shapes of narratives, which are independent of book length. We instead look at how words change over the course of a book as a function of the number of words, rather than the fraction of the book, completed at any given point; we de􀀌ne this measure as \\cumulative word-time\". Using ousiometrics, a reinterpretation of the valence-arousal-dominance framework of meaning obtained from semantic di􀀋erentials, we convert text into time series of power and danger scores, with time corresponding to cumulative word-time. Each time series is then decomposed using empirical mode decomposition into a sum of constituent oscillatory modes anda non-oscillatory trend. By comparing the decomposition of the original power and danger time series with those derived from shu􀀏ed text, we 􀀌nd that shorter books exhibit only a general trend, while longer books have 􀀍uctuations in addition to the general trend. These 􀀍uctuations typically have a period of a few thousand words regardless of the book length or library classi􀀌cation code, but vary depending on the content and structure of the book. Our 􀀌ndings suggest that, in theousiometric sense, longer books are not expanded versions of shorter books, but rather are more similar in structure to a concatenation of shorter texts. Further, they are consistent with editorial practices that require longer texts to be broken down into sections, such as chapters. Our method also provides a data-driven denoising approach that works for texts of various lengths, in contrast to the more traditional approach of using large window sizes that may inadvertently smooth out relevant information, especially for shorter texts. All together, these results open up avenues for future work in computational literary analysis, particularly the possibility of measuring a basic unit of narrative.  \nI. INTRODUCTION  \nThe computational study of word usage in text has mainly been con􀀌ned to aggregates, particularly the frequency of words in large corpora and contextual co-occurrence [Corral et al. 2015 , Dodds et al. 2020 , Reimers and Gurevych 2019 , Ryland Williams et al. 2015 , Vaswani et al. 2017] . Only recently have there been quantitative studies on how word usage changes over the course of a single text, and these have focused on the structure of narratives. Studies on narratives have traditionally been made from a qualitative perspective, requiring human inputs to interpret text [Brown and Tu 2020 , Freytag 1900 , Genette 1983 , Phelan and Rabinowitz 2012 , Ricoeur 1980 , Vonnegut 1999] . While the world's literary experts are far from being supplanted by computational analyses|and arguably never will be| their attentions cannot be scaled to study very large corpora, for which quantitative techniques have been developed.  \nGao et al. [Gao et al. 2016] showed that sentiment in novels have long-range correlations across the length of  \n􀀃 [mikaela.fudolig@uvm.edu](mikaela.fudolig@uvm.edu)  \nthe book, indicating that sentiment exhibits a structure that relates to the 􀀍ow of the novel. Inspired by Vonnegut [Vonnegut 1999 , 2010 , 2005], Reagan et al. [2016] examined how smoothed, \\distantly measu","cbCaitAV4A2ohUnX","https://ap.wps.com/l/cbCaitAV4A2ohUnX","pdf",6508901,26,"English","# Introduction\n## Background on word-usage studies\n## Narrative progression and sentiment arcs\n## Topic and embedding-based narrative representations","[{\"question\":\"What does the paper mean by cumulative word-time?\",\"answer\":\"Cumulative word-time indexes positions in a book by the number of words encountered, enabling analysis of how language changes over the course of the text independent of simple completion fraction.\"},{\"question\":\"How are power and danger time series derived from text?\",\"answer\":\"The method applies an ousiometric reinterpretation of the valence-arousal-dominance meaning framework and converts text into power and danger scores over time.\"},{\"question\":\"What key difference is found between shorter and longer books?\",\"answer\":\"Shorter books show mainly a general non-oscillatory trend, whereas longer books exhibit additional oscillatory fluctuations whose periods are typically a few thousand words and depend on content structure.\"}]","A decomposition of book structure through ousiometric fluctuations in cumulative word-time | PDF",66]