{"id":"https://openalex.org/W2570756609","doi":"https://doi.org/10.1109/besc.2016.7804470","title":"A multiscale theory for the dynamical evolution of sentiment in novels","display_name":"A multiscale theory for the dynamical evolution of sentiment in novels","publication_year":2016,"publication_date":"2016-11-01","ids":{"openalex":"https://openalex.org/W2570756609","doi":"https://doi.org/10.1109/besc.2016.7804470","mag":"2570756609"},"language":"en","primary_location":{"id":"doi:10.1109/besc.2016.7804470","is_oa":false,"landing_page_url":"https://doi.org/10.1109/besc.2016.7804470","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 International Conference on Behavioral, Economic and Socio-cultural Computing (BESC)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5103038001","display_name":"Jianbo Gao","orcid":null},"institutions":[{"id":"https://openalex.org/I150807315","display_name":"Guangxi University","ror":"https://ror.org/02c9qn167","country_code":"CN","type":"education","lineage":["https://openalex.org/I150807315"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianbo Gao","raw_affiliation_strings":["Institute of Complexity Science, Big Data Technology Guangxi University, Guangxi, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Complexity Science, Big Data Technology Guangxi University, Guangxi, China","institution_ids":["https://openalex.org/I150807315"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045067400","display_name":"Matthew L. Jockers","orcid":"https://orcid.org/0000-0001-5599-3706"},"institutions":[{"id":"https://openalex.org/I114395901","display_name":"University of Nebraska\u2013Lincoln","ror":"https://ror.org/043mer456","country_code":"US","type":"education","lineage":["https://openalex.org/I114395901"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Matthew L. Jockers","raw_affiliation_strings":["Department of English, University of Nebraska-Lincoln, Lincoln, NE"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of English, University of Nebraska-Lincoln, Lincoln, NE","institution_ids":["https://openalex.org/I114395901"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030862993","display_name":"John Laudun","orcid":"https://orcid.org/0000-0002-7555-7562"},"institutions":[{"id":"https://openalex.org/I79516672","display_name":"University of Louisiana at Lafayette","ror":"https://ror.org/01x8rc503","country_code":"US","type":"education","lineage":["https://openalex.org/I2799628689","https://openalex.org/I79516672"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"John Laudun","raw_affiliation_strings":["English Department, University of Louisiana at Lafayette, Lafayette, LA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"English Department, University of Louisiana at Lafayette, Lafayette, LA","institution_ids":["https://openalex.org/I79516672"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5037673211","display_name":"Timothy R. Tangherlini","orcid":"https://orcid.org/0000-0002-1775-2052"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Timothy Tangherlini","raw_affiliation_strings":["Department of Asian Languages and Cultures UCLA, CA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Asian Languages and Cultures UCLA, CA","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":6.8086,"has_fulltext":false,"cited_by_count":27,"citation_normalized_percentile":{"value":0.97836385,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"4"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12946","display_name":"Fractal and DNA sequence analysis","score":0.9941999912261963,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T12946","display_name":"Fractal and DNA sequence analysis","score":0.9941999912261963,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11270","display_name":"Complex Systems and Time Series Analysis","score":0.9807000160217285,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10244","display_name":"Chaos control and synchronization","score":0.9520999789237976,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/sentiment-analysis","display_name":"Sentiment analysis","score":0.7438371181488037},{"id":"https://openalex.org/keywords/multifractal-system","display_name":"Multifractal system","score":0.6932188868522644},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6679239869117737},{"id":"https://openalex.org/keywords/proxy","display_name":"Proxy (statistics)","score":0.5323463082313538},{"id":"https://openalex.org/keywords/smoothing","display_name":"Smoothing","score":0.5270838141441345},{"id":"https://openalex.org/keywords/hurst-exponent","display_name":"Hurst exponent","score":0.48535671830177307},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.47791677713394165},{"id":"https://openalex.org/keywords/fractal","display_name":"Fractal","score":0.43141692876815796},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.424926221370697},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.40502259135246277},{"id":"https://openalex.org/keywords/statistical-physics","display_name":"Statistical physics","score":0.3387405276298523},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.32113227248191833},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.21114999055862427},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.17328712344169617},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.12993043661117554},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.11258196830749512},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.09671235084533691},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.09274187684059143}],"concepts":[{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.7438371181488037},{"id":"https://openalex.org/C133905733","wikidata":"https://www.wikidata.org/wiki/Q2629238","display_name":"Multifractal system","level":3,"score":0.6932188868522644},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6679239869117737},{"id":"https://openalex.org/C2780148112","wikidata":"https://www.wikidata.org/wiki/Q1432581","display_name":"Proxy (statistics)","level":2,"score":0.5323463082313538},{"id":"https://openalex.org/C3770464","wikidata":"https://www.wikidata.org/wiki/Q775963","display_name":"Smoothing","level":2,"score":0.5270838141441345},{"id":"https://openalex.org/C96835011","wikidata":"https://www.wikidata.org/wiki/Q1638718","display_name":"Hurst exponent","level":2,"score":0.48535671830177307},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.47791677713394165},{"id":"https://openalex.org/C40636538","wikidata":"https://www.wikidata.org/wiki/Q81392","display_name":"Fractal","level":2,"score":0.43141692876815796},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.424926221370697},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.40502259135246277},{"id":"https://openalex.org/C121864883","wikidata":"https://www.wikidata.org/wiki/Q677916","display_name":"Statistical physics","level":1,"score":0.3387405276298523},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.32113227248191833},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.21114999055862427},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.17328712344169617},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.12993043661117554},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.11258196830749512},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.09671235084533691},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.09274187684059143},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/besc.2016.7804470","is_oa":false,"landing_page_url":"https://doi.org/10.1109/besc.2016.7804470","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 International Conference on Behavioral, Economic and Socio-cultural Computing (BESC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6899999976158142,"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W1967671229","https://openalex.org/W1990893695","https://openalex.org/W1993220186","https://openalex.org/W2011480164","https://openalex.org/W2016267254","https://openalex.org/W2021760137","https://openalex.org/W2044984635","https://openalex.org/W2050740215","https://openalex.org/W2057953526","https://openalex.org/W2065354509","https://openalex.org/W2078206416","https://openalex.org/W2088581336","https://openalex.org/W2114054266","https://openalex.org/W2117445034","https://openalex.org/W2119011327","https://openalex.org/W2127873098","https://openalex.org/W2149167588","https://openalex.org/W2155387468","https://openalex.org/W2306941105","https://openalex.org/W2313953460","https://openalex.org/W2479877058","https://openalex.org/W2488503934","https://openalex.org/W2964631455","https://openalex.org/W3100779964","https://openalex.org/W3103629061","https://openalex.org/W4399572298"],"related_works":["https://openalex.org/W1639315488","https://openalex.org/W4382488079","https://openalex.org/W1491355282","https://openalex.org/W2061574514","https://openalex.org/W2101329201","https://openalex.org/W3085694758","https://openalex.org/W4299355101","https://openalex.org/W1993609416","https://openalex.org/W1542327222","https://openalex.org/W3149493828"],"abstract_inverted_index":{"Recent":[0],"work":[1],"in":[2,10,22,66,133],"literary":[3],"sentiment":[4,26,53,88,120,125,131],"analysis":[5],"has":[6,148],"suggested":[7],"that":[8,82,117,137],"shifts":[9],"emotional":[11],"valence":[12],"may":[13],"serve":[14],"as":[15,95],"a":[16,30,37,70,96,100,118,139],"reliable":[17],"proxy":[18],"for":[19,138],"plot":[20],"movement":[21],"novels.":[23],"The":[24],"raw":[25],"time":[27,54],"series":[28],"of":[29,39,69,85,104,126],"novel":[31,140],"can":[32],"now":[33],"be":[34,142,150,156],"extracted":[35],"using":[36],"variety":[38],"different":[40],"methods,":[41],"and":[42,74,77,136,145],"after":[43],"extraction,":[44],"filtering":[45],"is":[46,61],"commonly":[47],"used":[48],"to":[49,123,141,149,159],"smooth":[50],"the":[51,63,83,86,92,124],"irregular":[52],"series.":[55],"Using":[56],"an":[57],"adaptive":[58],"filter,":[59],"which":[60,110],"among":[62],"most":[64],"effective":[65],"determining":[67],"trends":[68],"signal,":[71],"reducing":[72],"noise,":[73],"performing":[75],"fractal":[76],"multifractal":[78],"analysis,":[79],"we":[80],"show":[81,116],"energy":[84],"smoothed":[87,119],"signals":[89],"decays":[90],"with":[91],"smoothing":[93],"parameter":[94,102],"power-law,":[97],"characterized":[98],"by":[99],"Hurst":[101],"H":[103,107,147],"1/2":[105,153],"<;":[106,108],"1,":[109],"signifies":[111],"long-range":[112],"correlations.":[113],"We":[114],"further":[115],"arc":[121],"corresponds":[122],"fast":[127],"playing":[128],"mode":[129],"or":[130],"retained":[132],"one's":[134],"memory,":[135],"both":[143],"captivating":[144],"rich,":[146],"larger":[151],"than":[152],"but":[154],"cannot":[155],"too":[157],"close":[158],"1.":[160]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":4},{"year":2018,"cited_by_count":3},{"year":2017,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}