{"id":"https://openalex.org/W4400679605","doi":"https://doi.org/10.1109/access.2024.3429150","title":"A Hybrid Neural Network Model for Sentiment Analysis of Financial Texts Using Topic Extraction, Pre-Trained Model, and Enhanced Attention Mechanism Methods","display_name":"A Hybrid Neural Network Model for Sentiment Analysis of Financial Texts Using Topic Extraction, Pre-Trained Model, and Enhanced Attention Mechanism Methods","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W4400679605","doi":"https://doi.org/10.1109/access.2024.3429150"},"language":"en","primary_location":{"id":"doi:10.1109/access.2024.3429150","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3429150","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":null,"license_id":null,"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.2024.3429150","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5090232371","display_name":"Ganglong Duan","orcid":"https://orcid.org/0000-0001-8822-3177"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ganglong Duan","raw_affiliation_strings":["School of Economics and Management, Xi&#x2019;an University of Technology, Xi&#x2019;an, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Economics and Management, Xi&#x2019;an University of Technology, Xi&#x2019;an, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101351049","display_name":"Shunfei Yan","orcid":"https://orcid.org/0009-0001-7311-475X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shunfei Yan","raw_affiliation_strings":["School of Economics and Management, Xi&#x2019;an University of Technology, Xi&#x2019;an, China"],"raw_orcid":"https://orcid.org/0009-0001-7311-475X","affiliations":[{"raw_affiliation_string":"School of Economics and Management, Xi&#x2019;an University of Technology, Xi&#x2019;an, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100437831","display_name":"Meng Zhang","orcid":"https://orcid.org/0009-0008-1255-4592"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Meng Zhang","raw_affiliation_strings":["School of Economics and Management, Xi&#x2019;an University of Technology, Xi&#x2019;an, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Economics and Management, Xi&#x2019;an University of Technology, Xi&#x2019;an, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"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":4.9078,"has_fulltext":false,"cited_by_count":20,"citation_normalized_percentile":{"value":0.95654008,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":"12","issue":null,"first_page":"98207","last_page":"98224"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9979000091552734,"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"}},"topics":[{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9979000091552734,"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"}},{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9958999752998352,"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/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9955000281333923,"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/sentiment-analysis","display_name":"Sentiment analysis","score":0.7840995788574219},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7667730450630188},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6266200542449951},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5227972865104675},{"id":"https://openalex.org/keywords/mechanism","display_name":"Mechanism (biology)","score":0.4323924779891968},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.34735745191574097},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.33404654264450073}],"concepts":[{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.7840995788574219},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7667730450630188},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6266200542449951},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5227972865104675},{"id":"https://openalex.org/C89611455","wikidata":"https://www.wikidata.org/wiki/Q6804646","display_name":"Mechanism (biology)","level":2,"score":0.4323924779891968},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34735745191574097},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.33404654264450073},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2024.3429150","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3429150","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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:a6a363bb78ce42ecafc5e223bcb1f212","is_oa":true,"landing_page_url":"https://doaj.org/article/a6a363bb78ce42ecafc5e223bcb1f212","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":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 12, Pp 98207-98224 (2024)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2024.3429150","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3429150","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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":49,"referenced_works":["https://openalex.org/W1546425147","https://openalex.org/W1965235124","https://openalex.org/W2004830941","https://openalex.org/W2094062137","https://openalex.org/W2113855231","https://openalex.org/W2329588002","https://openalex.org/W2401143219","https://openalex.org/W2517194566","https://openalex.org/W2523366393","https://openalex.org/W2734498027","https://openalex.org/W2745672448","https://openalex.org/W2753259282","https://openalex.org/W2798658104","https://openalex.org/W2803762112","https://openalex.org/W2896457183","https://openalex.org/W2955764038","https://openalex.org/W2996428491","https://openalex.org/W3013505582","https://openalex.org/W3035101152","https://openalex.org/W3043424630","https://openalex.org/W3115467802","https://openalex.org/W3138934474","https://openalex.org/W3167958315","https://openalex.org/W3182414949","https://openalex.org/W4210827551","https://openalex.org/W4221125416","https://openalex.org/W4226053196","https://openalex.org/W4231510805","https://openalex.org/W4317930845","https://openalex.org/W4318561958","https://openalex.org/W4324030804","https://openalex.org/W4324140899","https://openalex.org/W4327969336","https://openalex.org/W4362558246","https://openalex.org/W4378471278","https://openalex.org/W4379539262","https://openalex.org/W4381597630","https://openalex.org/W4382998416","https://openalex.org/W4388335799","https://openalex.org/W4388994251","https://openalex.org/W4389032255","https://openalex.org/W4391335255","https://openalex.org/W4392168447","https://openalex.org/W4395101985","https://openalex.org/W6639619044","https://openalex.org/W6755207826","https://openalex.org/W6768021236","https://openalex.org/W6798398338","https://openalex.org/W6853413704"],"related_works":["https://openalex.org/W3089396779","https://openalex.org/W2548633793","https://openalex.org/W3013279174","https://openalex.org/W2941935829","https://openalex.org/W2596247554","https://openalex.org/W4301373556","https://openalex.org/W3132372214","https://openalex.org/W4224284088","https://openalex.org/W4286571989","https://openalex.org/W2765903680"],"abstract_inverted_index":{"In":[0,84],"the":[1,16,44,78,107,115,118,123,127,153,159,162,166,169,177,189,193,203,215],"financial":[2,53,67,82,99,222],"field,":[3],"texts":[4],"such":[5,61],"as":[6,10,62,145],"news":[7],"and":[8,25,28,50,71,74,96,104,151,184],"commentaries,":[9],"carriers":[11],"of":[12,18,47,66,77,81,109,130,155,161,168,173,195,208],"public":[13],"opinion,":[14],"have":[15],"function":[17],"reflecting":[19],"investor":[20],"sentiment,":[21],"influencing":[22],"investment":[23,45],"decisions":[24,46],"market":[26],"trends,":[27],"extracting":[29],"positive":[30],"or":[31],"negative":[32],"sentiment":[33,54,64,101,224],"from":[34],"them":[35],"in":[36,188,210],"a":[37,92,133],"timely":[38],"manner":[39],"is":[40,56,219],"very":[41],"important":[42],"to":[43,59,86,113,132,146,179,191],"fintech":[48],"companies":[49],"investors.":[51],"However,":[52],"analysis":[55,102],"challenging":[57],"due":[58],"issues":[60],"unclear":[63],"polarity":[65],"texts,":[68],"high":[69],"context-dependency,":[70],"highly":[72],"specialised":[73],"specific":[75],"expressions":[76],"linguistic":[79],"features":[80,175],"texts.":[83],"order":[85],"overcome":[87],"these":[88],"challenges,":[89],"we":[90,105],"design":[91,106],"hybrid":[93],"topic":[94],"feature":[95],"pre-trained":[97],"model":[98,116,178],"text":[100,190,196,223],"model,":[103],"method":[108,204],"improved":[110,119],"attention":[111,120,131,138,140],"mechanism":[112,121],"optimise":[114],"iteratively,":[117],"reduces":[122],"noise":[124],"caused":[125],"by":[126],"improper":[128],"allocation":[129],"single":[134],"word":[135,186],"through":[136],"adaptive":[137],"threshold,":[139],"weight":[141],"masking":[142],"mechanism,":[143],"so":[144],"capture":[147,180],"more":[148],"contextual":[149],"information":[150],"avoid":[152],"loss":[154],"information,":[156],"thus":[157],"improving":[158],"stability":[160,167],"model.":[163,170],"This":[164],"improves":[165],"The":[171,198],"introduction":[172],"thematic":[174],"enables":[176],"potential":[181],"semantic":[182],"structures":[183],"long-distance":[185],"associations":[187],"enhance":[192],"understanding":[194],"context.":[197],"experimental":[199],"results":[200],"show":[201],"that":[202],"has":[205],"an":[206],"improvement":[207],"2.05%-7.27%":[209],"F1":[211],"value":[212],"compared":[213],"with":[214],"baseline":[216],"method,":[217],"which":[218],"suitable":[220],"for":[221],"analysis.":[225]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":15},{"year":2024,"cited_by_count":2}],"updated_date":"2026-07-16T13:24:37.021932","created_date":"2025-10-10T00:00:00"}
