{"id":"https://openalex.org/W4318147446","doi":"https://doi.org/10.1109/bigdata55660.2022.10020562","title":"SFPAD: Sentiment-driven Firm Pricing Anomaly Detection Algorithm and Application","display_name":"SFPAD: Sentiment-driven Firm Pricing Anomaly Detection Algorithm and Application","publication_year":2022,"publication_date":"2022-12-17","ids":{"openalex":"https://openalex.org/W4318147446","doi":"https://doi.org/10.1109/bigdata55660.2022.10020562"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata55660.2022.10020562","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/bigdata55660.2022.10020562","pdf_url":null,"source":{"id":"https://openalex.org/S4363607709","display_name":"2022 IEEE International Conference on Big Data (Big Data)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Big Data (Big Data)","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/A5088960246","display_name":"Senyuan Zheng","orcid":null},"institutions":[{"id":"https://openalex.org/I56590836","display_name":"Monash University","ror":"https://ror.org/02bfwt286","country_code":"AU","type":"education","lineage":["https://openalex.org/I56590836"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Senyuan Zheng","raw_affiliation_strings":["FIT Monash University,DS & AI Department,Melbourne,VIC,Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"FIT Monash University,DS & AI Department,Melbourne,VIC,Australia","institution_ids":["https://openalex.org/I56590836"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045979794","display_name":"Vincent C. S. Lee","orcid":"https://orcid.org/0000-0001-5976-4601"},"institutions":[{"id":"https://openalex.org/I56590836","display_name":"Monash University","ror":"https://ror.org/02bfwt286","country_code":"AU","type":"education","lineage":["https://openalex.org/I56590836"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Vincent CS Lee","raw_affiliation_strings":["FIT Monash University,DS & AI Department,Melbourne,VIC,Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"FIT Monash University,DS & AI Department,Melbourne,VIC,Australia","institution_ids":["https://openalex.org/I56590836"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5090447916","display_name":"Zhaolin Guan","orcid":null},"institutions":[{"id":"https://openalex.org/I165779595","display_name":"The University of Melbourne","ror":"https://ror.org/01ej9dk98","country_code":"AU","type":"education","lineage":["https://openalex.org/I165779595"]},{"id":"https://openalex.org/I2802046543","display_name":"Victorian Curriculum and Assessment Authority","ror":"https://ror.org/02w6dpq08","country_code":"AU","type":"government","lineage":["https://openalex.org/I2801244131","https://openalex.org/I2802046543","https://openalex.org/I4210132500"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Zhaolin Guan","raw_affiliation_strings":["University of Melbourne,Melbourne Business School,Melbourne,VIC,Australia","Melbourne Business School, University of Melbourne, Melbourne, VIC, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Melbourne,Melbourne Business School,Melbourne,VIC,Australia","institution_ids":["https://openalex.org/I165779595","https://openalex.org/I2802046543"]},{"raw_affiliation_string":"Melbourne Business School, University of Melbourne, Melbourne, VIC, Australia","institution_ids":["https://openalex.org/I165779595"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"3567","last_page":"3576"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11270","display_name":"Complex Systems and Time Series Analysis","score":0.9957000017166138,"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/T10047","display_name":"Financial Markets and Investment Strategies","score":0.9950000047683716,"subfield":{"id":"https://openalex.org/subfields/2003","display_name":"Finance"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.7267343997955322},{"id":"https://openalex.org/keywords/sentiment-analysis","display_name":"Sentiment analysis","score":0.6015800833702087},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5821461081504822},{"id":"https://openalex.org/keywords/stock","display_name":"Stock (firearms)","score":0.5764297246932983},{"id":"https://openalex.org/keywords/stock-market","display_name":"Stock market","score":0.5401768684387207},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.5311592817306519},{"id":"https://openalex.org/keywords/stock-price","display_name":"Stock price","score":0.48406949639320374},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.44791117310523987},{"id":"https://openalex.org/keywords/social-media","display_name":"Social media","score":0.4140762984752655},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.40550461411476135},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.39349016547203064},{"id":"https://openalex.org/keywords/financial-economics","display_name":"Financial economics","score":0.37917566299438477},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.3364930748939514},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3341918885707855},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3222191333770752},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.28840625286102295},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.10691818594932556},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.0980488657951355},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.08273163437843323}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.7267343997955322},{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.6015800833702087},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5821461081504822},{"id":"https://openalex.org/C204036174","wikidata":"https://www.wikidata.org/wiki/Q909380","display_name":"Stock (firearms)","level":2,"score":0.5764297246932983},{"id":"https://openalex.org/C2780299701","wikidata":"https://www.wikidata.org/wiki/Q475000","display_name":"Stock market","level":3,"score":0.5401768684387207},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.5311592817306519},{"id":"https://openalex.org/C2988984586","wikidata":"https://www.wikidata.org/wiki/Q1020013","display_name":"Stock price","level":3,"score":0.48406949639320374},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.44791117310523987},{"id":"https://openalex.org/C518677369","wikidata":"https://www.wikidata.org/wiki/Q202833","display_name":"Social media","level":2,"score":0.4140762984752655},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.40550461411476135},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.39349016547203064},{"id":"https://openalex.org/C106159729","wikidata":"https://www.wikidata.org/wiki/Q2294553","display_name":"Financial economics","level":1,"score":0.37917566299438477},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.3364930748939514},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3341918885707855},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3222191333770752},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.28840625286102295},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.10691818594932556},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.0980488657951355},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.08273163437843323},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C26873012","wikidata":"https://www.wikidata.org/wiki/Q214781","display_name":"Condensed matter physics","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C2780762169","wikidata":"https://www.wikidata.org/wiki/Q5905368","display_name":"Horse","level":2,"score":0.0},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata55660.2022.10020562","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/bigdata55660.2022.10020562","pdf_url":null,"source":{"id":"https://openalex.org/S4363607709","display_name":"2022 IEEE International Conference on Big Data (Big Data)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":46,"referenced_works":["https://openalex.org/W2018165284","https://openalex.org/W2121470887","https://openalex.org/W2147347448","https://openalex.org/W2184801043","https://openalex.org/W2337344967","https://openalex.org/W2741783723","https://openalex.org/W2778728785","https://openalex.org/W2896248782","https://openalex.org/W2899323951","https://openalex.org/W2964036159","https://openalex.org/W2992136964","https://openalex.org/W3005299290","https://openalex.org/W3011742849","https://openalex.org/W3014106621","https://openalex.org/W3044439675","https://openalex.org/W3095011517","https://openalex.org/W3104083384","https://openalex.org/W3113083505","https://openalex.org/W3121541046","https://openalex.org/W3122183745","https://openalex.org/W3123150828","https://openalex.org/W3123465443","https://openalex.org/W3123725094","https://openalex.org/W3124807941","https://openalex.org/W3125512880","https://openalex.org/W3130787562","https://openalex.org/W3133159916","https://openalex.org/W3136378589","https://openalex.org/W3137569071","https://openalex.org/W3145799058","https://openalex.org/W3146041904","https://openalex.org/W3160203461","https://openalex.org/W3196200569","https://openalex.org/W4206945301","https://openalex.org/W4243752770","https://openalex.org/W4252451855","https://openalex.org/W4285579224","https://openalex.org/W4294567033","https://openalex.org/W4392846541","https://openalex.org/W6603405191","https://openalex.org/W6633214182","https://openalex.org/W6689887583","https://openalex.org/W6728046525","https://openalex.org/W6734312481","https://openalex.org/W6745863872","https://openalex.org/W6775788394"],"related_works":["https://openalex.org/W2806741695","https://openalex.org/W3210364259","https://openalex.org/W4290647774","https://openalex.org/W3189286258","https://openalex.org/W3207797160","https://openalex.org/W2667207928","https://openalex.org/W2912112202","https://openalex.org/W4300558037","https://openalex.org/W4377864969","https://openalex.org/W3030345572"],"abstract_inverted_index":{"In":[0,118],"January":[1],"of":[2,7,16,29,77,115],"2021,":[3],"the":[4,14,26,51,68,87,101,113,121],"stock":[5,36],"price":[6],"GameStop":[8,88],"Corp":[9],"ascended":[10],"20":[11],"times":[12],"under":[13],"influence":[15],"social":[17],"media":[18],"in":[19,67],"a":[20,73],"mere":[21],"two":[22],"weeks\u2019":[23],"time,":[24],"becoming":[25],"first-ever":[27],"case":[28],"sentiment-driven":[30,79,116],"short":[31],"squeeze.":[32],"This":[33],"caused":[34],"certain":[35],"prices":[37],"to":[38,62,105,112,129,136],"deviate":[39],"significantly":[40],"from":[41,86],"their":[42],"fundamental":[43],"value,":[44],"and":[45,59],"hence,":[46],"had":[47],"an":[48],"impact":[49],"on":[50],"market":[52],"efficiency.":[53],"To":[54],"help":[55],"finance":[56],"policymakers,":[57],"regulators,":[58],"investment":[60],"analysts":[61],"prepare":[63],"for":[64],"cases":[65],"alike":[66],"future,":[69],"our":[70,119],"study":[71],"investigates":[72],"Machine":[74,123],"Learning":[75,124],"method":[76,92,126],"detecting":[78],"firm":[80],"pricing":[81],"anomaly":[82,94],"using":[83],"data":[84],"collected":[85],"frenzy.":[89],"The":[90],"proposed":[91],"combines":[93],"pattern":[95],"detection":[96,132],"with":[97],"sentiment":[98],"analysis,":[99],"which":[100],"latter":[102],"is":[103],"used":[104],"identify":[106],"conditions":[107],"that":[108],"are":[109],"considered":[110],"crucial":[111],"presence":[114],"pricing.":[117],"experiments,":[120],"adapted":[122],"(SFPAD)":[125],"was":[127],"able":[128],"outperform":[130],"traditional":[131],"technique":[133],"when":[134],"applied":[135],"historical":[137],"time":[138],"series.":[139]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}