{"id":"https://openalex.org/W7163715230","doi":"https://doi.org/10.1109/access.2026.3700831","title":"AI-Enhanced Financial NLP for Bitcoin Return Variation Prediction: Timestamp-Aware Evidence From Trump\u2019s Tweets","display_name":"AI-Enhanced Financial NLP for Bitcoin Return Variation Prediction: Timestamp-Aware Evidence From Trump\u2019s Tweets","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7163715230","doi":"https://doi.org/10.1109/access.2026.3700831"},"language":"en","primary_location":{"id":"doi:10.1109/access.2026.3700831","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3700831","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2026.3700831","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5137997588","display_name":"Liang Hu","orcid":"https://orcid.org/0009-0005-6930-236X"},"institutions":[{"id":"https://openalex.org/I78577930","display_name":"Columbia University","ror":"https://ror.org/00hj8s172","country_code":"US","type":"education","lineage":["https://openalex.org/I78577930"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Liang Hu","raw_affiliation_strings":["Department of Computer Science, Columbia University, New York, NY, USA"],"raw_orcid":"https://orcid.org/0009-0005-6930-236X","affiliations":[{"raw_affiliation_string":"Department of Computer Science, Columbia University, New York, NY, USA","institution_ids":["https://openalex.org/I78577930"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5109773312","display_name":"Yinru Shen","orcid":null},"institutions":[{"id":"https://openalex.org/I103163165","display_name":"Florida State University","ror":"https://ror.org/05g3dte14","country_code":"US","type":"education","lineage":["https://openalex.org/I103163165"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yinru Shen","raw_affiliation_strings":["Department of Mathematics, Florida State University, Tallahassee, FL, USA"],"raw_orcid":"https://orcid.org/0009-0006-5523-4156","affiliations":[{"raw_affiliation_string":"Department of Mathematics, Florida State University, Tallahassee, FL, USA","institution_ids":["https://openalex.org/I103163165"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.73865851,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"14","issue":null,"first_page":"88558","last_page":"88565"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.4609000086784363,"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":0.4609000086784363,"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/T10270","display_name":"Blockchain Technology Applications and Security","score":0.21719999611377716,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.026200000196695328,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/variation","display_name":"Variation (astronomy)","score":0.7626000046730042},{"id":"https://openalex.org/keywords/named-entity","display_name":"Named entity","score":0.27469998598098755},{"id":"https://openalex.org/keywords/computational-linguistics","display_name":"Computational linguistics","score":0.23029999434947968},{"id":"https://openalex.org/keywords/sentiment-analysis","display_name":"Sentiment analysis","score":0.2084999978542328}],"concepts":[{"id":"https://openalex.org/C2778334786","wikidata":"https://www.wikidata.org/wiki/Q1586270","display_name":"Variation (astronomy)","level":2,"score":0.7626000046730042},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7199000120162964},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6353999972343445},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5838000178337097},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.3154999911785126},{"id":"https://openalex.org/C2777889803","wikidata":"https://www.wikidata.org/wiki/Q25047676","display_name":"Named entity","level":2,"score":0.27469998598098755},{"id":"https://openalex.org/C155092808","wikidata":"https://www.wikidata.org/wiki/Q182557","display_name":"Computational linguistics","level":2,"score":0.23029999434947968},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2176000028848648},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.21570000052452087},{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.2084999978542328}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2026.3700831","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3700831","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:1ace064500304a65836f5448d2bb2f71","is_oa":false,"landing_page_url":"https://doaj.org/article/1ace064500304a65836f5448d2bb2f71","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 14, Pp 88558-88565 (2026)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2026.3700831","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3700831","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/8","display_name":"Decent work and economic growth","score":0.44720348715782166}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"This":[0],"paper":[1],"examines":[2],"whether":[3],"textual":[4],"features":[5,37,201,229],"derived":[6],"from":[7],"Donald":[8],"J.":[9],"Trump\u2019s":[10],"tweets":[11],"contain":[12],"predictive":[13,105],"information":[14],"about":[15],"subsequent":[16],"Bitcoin":[17,42],"return":[18],"variation":[19],"under":[20],"leakage-resistant":[21],"temporal":[22],"validation.":[23],"Using":[24],"an":[25],"archive":[26],"of":[27,196],"92,613":[28],"tweets,":[29],"we":[30],"construct":[31],"sentiment,":[32],"confidence,":[33],"readability,":[34],"and":[35,38,43,87,114,138,157,173,202,217],"part-of-speech":[36],"combine":[39],"them":[40],"with":[41,101,111],"U.S.":[44],"Dollar":[45],"market":[46],"variables.":[47],"The":[48,180],"analysis":[49],"is":[50],"organized":[51],"into":[52],"four":[53],"empirical":[54],"layers:":[55],"Study":[56,63,72,88,124,148,186],"A,":[57,125],"a":[58,65,90,212],"daily":[59,68,78,147,160],"aggregated":[60],"robustness":[61],"check;":[62],"B,":[64,73],"conservative":[66,145],"timestamp-aware":[67,146],"event-level":[69],"baseline;":[70],"Enhanced":[71,159],"which":[74],"keeps":[75],"the":[76,98,144,151,169,176,209,222],"same":[77],"event":[79],"samples":[80],"but":[81],"adds":[82],"richer":[83],"BTC/USD":[84],"market-history":[85,219],"features;":[86],"C,":[89,187],"4-hour":[91,189],"hourly-label":[92],"extension":[93],"that":[94],"more":[95],"closely":[96],"aligns":[97],"prediction":[99],"target":[100],"tweet":[102],"timing.":[103],"All":[104],"experiments":[106],"use":[107],"chronological":[108],"walk-forward":[109],"validation,":[110],"model":[112],"tuning":[113],"threshold":[115],"selection":[116],"performed":[117],"only":[118],"inside":[119],"historical":[120],"training":[121],"windows.":[122],"In":[123,143],"mean":[126,164,193],"ROC-AUC":[127,165,194],"values":[128,153,195],"are":[129,154,230],"0.5528":[130],"for":[131,135,140,168,175,198,204],"market-only":[132],"features,":[133,137],"0.5513":[134],"text-only":[136],"0.5481":[139],"combined":[141],"features.":[142,207],"B":[149],"baseline,":[150],"corresponding":[152],"0.4716,":[155],"0.4432,":[156],"0.4335.":[158],"logistic":[161],"specifications":[162],"improve":[163],"to":[166],"0.5787":[167],"BTC-core":[170],"feature":[171,178],"set":[172],"0.5756":[174],"combined-core":[177,206],"set.":[179],"strongest":[181],"average":[182],"performance":[183],"appears":[184],"in":[185],"where":[188],"hourly":[190,199,205],"labels":[191],"yield":[192],"0.7359":[197],"market-core":[200],"0.6698":[203],"Overall,":[208],"evidence":[210],"supports":[211],"predictive-association":[213],"interpretation:":[214],"timing":[215],"alignment":[216],"recent":[218],"variables":[220],"provide":[221],"most":[223],"stable":[224],"signal,":[225],"while":[226],"tweet-derived":[227],"sentiment":[228],"best":[231],"interpreted":[232],"as":[233],"contextual":[234],"predictors.":[235]},"counts_by_year":[],"updated_date":"2026-06-18T08:10:14.011955","created_date":"2026-06-06T00:00:00"}