{"id":"https://openalex.org/W4416113961","doi":"https://doi.org/10.1109/access.2025.3631665","title":"Modeling and Optimization of News-Stock Price Correlation Based on Topic Influence Selection","display_name":"Modeling and Optimization of News-Stock Price Correlation Based on Topic Influence Selection","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4416113961","doi":"https://doi.org/10.1109/access.2025.3631665"},"language":"en","primary_location":{"id":"doi:10.1109/access.2025.3631665","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3631665","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.2025.3631665","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5020457377","display_name":"Huaxi Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I186272606","display_name":"Guangdong University of Foreign Studies","ror":"https://ror.org/00fhc9y79","country_code":"CN","type":"education","lineage":["https://openalex.org/I186272606"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Huaxi Liu","raw_affiliation_strings":["School of Information Science and Technology, Guangdong University of Foreign Studies, Guangzhou, China"],"raw_orcid":"https://orcid.org/0009-0003-8058-3225","affiliations":[{"raw_affiliation_string":"School of Information Science and Technology, Guangdong University of Foreign Studies, Guangzhou, China","institution_ids":["https://openalex.org/I186272606"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Zonghan Jiang","orcid":"https://orcid.org/0009-0001-7992-0859"},"institutions":[{"id":"https://openalex.org/I186272606","display_name":"Guangdong University of Foreign Studies","ror":"https://ror.org/00fhc9y79","country_code":"CN","type":"education","lineage":["https://openalex.org/I186272606"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zonghan Jiang","raw_affiliation_strings":["School of Information Science and Technology, Guangdong University of Foreign Studies, Guangzhou, China"],"raw_orcid":"https://orcid.org/0009-0001-7992-0859","affiliations":[{"raw_affiliation_string":"School of Information Science and Technology, Guangdong University of Foreign Studies, Guangzhou, China","institution_ids":["https://openalex.org/I186272606"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100770502","display_name":"Desheng Li","orcid":"https://orcid.org/0009-0007-4200-077X"},"institutions":[{"id":"https://openalex.org/I111753288","display_name":"Fujian Normal University","ror":"https://ror.org/020azk594","country_code":"CN","type":"education","lineage":["https://openalex.org/I111753288"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Desheng Li","raw_affiliation_strings":["College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou, China"],"raw_orcid":"https://orcid.org/0009-0007-4200-077X","affiliations":[{"raw_affiliation_string":"College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou, China","institution_ids":["https://openalex.org/I111753288"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Lin Gao","orcid":"https://orcid.org/0009-0000-4346-3222"},"institutions":[{"id":"https://openalex.org/I27357992","display_name":"Dalian University of Technology","ror":"https://ror.org/023hj5876","country_code":"CN","type":"education","lineage":["https://openalex.org/I27357992"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lin Gao","raw_affiliation_strings":["School of Software, Dalian University of Technology, Dalian, China"],"raw_orcid":"https://orcid.org/0009-0000-4346-3222","affiliations":[{"raw_affiliation_string":"School of Software, Dalian University of Technology, Dalian, China","institution_ids":["https://openalex.org/I27357992"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100366701","display_name":"Kun Wang","orcid":"https://orcid.org/0000-0003-0487-2124"},"institutions":[{"id":"https://openalex.org/I111753288","display_name":"Fujian Normal University","ror":"https://ror.org/020azk594","country_code":"CN","type":"education","lineage":["https://openalex.org/I111753288"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kun Wang","raw_affiliation_strings":["College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou, China"],"raw_orcid":"https://orcid.org/0000-0003-0487-2124","affiliations":[{"raw_affiliation_string":"College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou, China","institution_ids":["https://openalex.org/I111753288"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5020457377"],"corresponding_institution_ids":["https://openalex.org/I186272606"],"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":0.37367445,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"13","issue":null,"first_page":"196127","last_page":"196142"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.7387999892234802,"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":0.7387999892234802,"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/T10047","display_name":"Financial Markets and Investment Strategies","score":0.060100000351667404,"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"}},{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.021199999377131462,"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/relevance","display_name":"Relevance (law)","score":0.524399995803833},{"id":"https://openalex.org/keywords/granger-causality","display_name":"Granger causality","score":0.5084999799728394},{"id":"https://openalex.org/keywords/sentiment-analysis","display_name":"Sentiment analysis","score":0.49470001459121704},{"id":"https://openalex.org/keywords/stock","display_name":"Stock (firearms)","score":0.4733999967575073},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.4262999892234802},{"id":"https://openalex.org/keywords/stock-price","display_name":"Stock price","score":0.40849998593330383},{"id":"https://openalex.org/keywords/stock-market","display_name":"Stock market","score":0.36890000104904175},{"id":"https://openalex.org/keywords/model-selection","display_name":"Model selection","score":0.35249999165534973},{"id":"https://openalex.org/keywords/yield","display_name":"Yield (engineering)","score":0.33640000224113464},{"id":"https://openalex.org/keywords/market-research","display_name":"Market research","score":0.3325999975204468}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6467999815940857},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.541100025177002},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.524399995803833},{"id":"https://openalex.org/C129824826","wikidata":"https://www.wikidata.org/wiki/Q2630107","display_name":"Granger causality","level":2,"score":0.5084999799728394},{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.49470001459121704},{"id":"https://openalex.org/C204036174","wikidata":"https://www.wikidata.org/wiki/Q909380","display_name":"Stock (firearms)","level":2,"score":0.4733999967575073},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44119998812675476},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.4262999892234802},{"id":"https://openalex.org/C2988984586","wikidata":"https://www.wikidata.org/wiki/Q1020013","display_name":"Stock price","level":3,"score":0.40849998593330383},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.37779998779296875},{"id":"https://openalex.org/C2780299701","wikidata":"https://www.wikidata.org/wiki/Q475000","display_name":"Stock market","level":3,"score":0.36890000104904175},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.35249999165534973},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34769999980926514},{"id":"https://openalex.org/C134121241","wikidata":"https://www.wikidata.org/wiki/Q899301","display_name":"Yield (engineering)","level":2,"score":0.33640000224113464},{"id":"https://openalex.org/C27564746","wikidata":"https://www.wikidata.org/wiki/Q913709","display_name":"Market research","level":2,"score":0.3325999975204468},{"id":"https://openalex.org/C2985708247","wikidata":"https://www.wikidata.org/wiki/Q173799","display_name":"Entertainment industry","level":3,"score":0.3319999873638153},{"id":"https://openalex.org/C171686336","wikidata":"https://www.wikidata.org/wiki/Q3532085","display_name":"Topic model","level":2,"score":0.3255999982357025},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.32359999418258667},{"id":"https://openalex.org/C512170562","wikidata":"https://www.wikidata.org/wiki/Q173799","display_name":"Entertainment","level":2,"score":0.31619998812675476},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.31279999017715454},{"id":"https://openalex.org/C163068380","wikidata":"https://www.wikidata.org/wiki/Q3409313","display_name":"Economic forecasting","level":2,"score":0.31150001287460327},{"id":"https://openalex.org/C64357122","wikidata":"https://www.wikidata.org/wiki/Q1149766","display_name":"Causality (physics)","level":2,"score":0.3095000088214874},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.30399999022483826},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3027999997138977},{"id":"https://openalex.org/C33566652","wikidata":"https://www.wikidata.org/wiki/Q1065927","display_name":"Theme (computing)","level":2,"score":0.2892000079154968},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.2872999906539917},{"id":"https://openalex.org/C202353208","wikidata":"https://www.wikidata.org/wiki/Q1167393","display_name":"Economic indicator","level":2,"score":0.28529998660087585},{"id":"https://openalex.org/C87007009","wikidata":"https://www.wikidata.org/wiki/Q210832","display_name":"Statistical hypothesis testing","level":2,"score":0.2847999930381775},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.28130000829696655},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.2777999937534332},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.274399995803833},{"id":"https://openalex.org/C93692415","wikidata":"https://www.wikidata.org/wiki/Q1502030","display_name":"Thematic map","level":2,"score":0.2709999978542328},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.2689000070095062},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.26499998569488525},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.25870001316070557},{"id":"https://openalex.org/C42475967","wikidata":"https://www.wikidata.org/wiki/Q194292","display_name":"Operations research","level":1,"score":0.2565000057220459},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.2551000118255615},{"id":"https://openalex.org/C158600405","wikidata":"https://www.wikidata.org/wiki/Q5054566","display_name":"Causal inference","level":2,"score":0.25369998812675476}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2025.3631665","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3631665","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:6c0bc5f15cc04427be9e4159efb03bcd","is_oa":true,"landing_page_url":"https://doaj.org/article/6c0bc5f15cc04427be9e4159efb03bcd","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 13, Pp 196127-196142 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2025.3631665","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3631665","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":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W1902027874","https://openalex.org/W2017812666","https://openalex.org/W2053615983","https://openalex.org/W2086694651","https://openalex.org/W2149684865","https://openalex.org/W2168039431","https://openalex.org/W2171468534","https://openalex.org/W2308962670","https://openalex.org/W2342352817","https://openalex.org/W2605386549","https://openalex.org/W2741908740","https://openalex.org/W2782630477","https://openalex.org/W2887963817","https://openalex.org/W2900772751","https://openalex.org/W2979602634","https://openalex.org/W3122563224","https://openalex.org/W3156333129","https://openalex.org/W4380551660","https://openalex.org/W4385245566","https://openalex.org/W4387430343","https://openalex.org/W4389045291","https://openalex.org/W4392912932","https://openalex.org/W4406464349","https://openalex.org/W4409197419"],"related_works":[],"abstract_inverted_index":{"This":[0],"study":[1],"examines":[2],"the":[3,33,155],"evolving":[4],"relationship":[5],"between":[6],"news":[7,19,30,44,91,99,112],"topics":[8],"and":[9,40,49,62,108,140],"stock":[10,81],"prices,":[11],"offering":[12],"a":[13,26,84,124,150],"quantitative":[14,125],"analysis":[15],"of":[16,28],"how":[17],"thematic":[18],"coverage":[20],"influences":[21],"market":[22],"volatility.":[23],"Drawing":[24],"on":[25,80],"dataset":[27],"90,000":[29],"headlines":[31],"from":[32],"Dow":[34],"Jones":[35],"industrial":[36],"average":[37],"spanning":[38],"2008-2016":[39],"2018-2023,":[41,104],"we":[42,72,122],"classified":[43],"into":[45],"political,":[46],"technological,":[47],"entertainment,":[48],"other":[50],"categories,":[51],"with":[52,69,149],"each":[53],"theme":[54],"further":[55],"divided":[56],"by":[57],"sentiment":[58,152],"polarity":[59],"(negative,":[60],"neutral,":[61],"positive).":[63],"Using":[64],"Granger":[65],"causality":[66],"tests":[67],"combined":[68],"time-period":[70],"segmentation,":[71],"found":[73],"that":[74],"certain":[75],"categories":[76],"exert":[77],"significant":[78],"effects":[79],"movements":[82],"within":[83],"1-3":[85],"day":[86],"lag.":[87],"Specifically,":[88],"negative":[89],"entertainment":[90],"showed":[92],"long-term":[93,109],"relevance":[94],"during":[95],"2008-2016,":[96],"while":[97],"health-related":[98],"gained":[100],"notable":[101],"importance":[102],"in":[103,130],"influencing":[105],"both":[106],"short-":[107],"dynamics;":[110],"political":[111],"likewise":[113],"revealed":[114],"strong":[115],"market-driving":[116],"potential.":[117],"To":[118],"formalize":[119],"these":[120],"findings,":[121],"introduce":[123],"\u201ctopic":[126],"impact\u201d":[127],"framework":[128],"grounded":[129],"topic-stock":[131],"price":[132],"causality,":[133],"integrating":[134],"natural":[135],"language":[136],"processing,":[137],"statistical":[138],"modeling,":[139],"deep":[141],"learning":[142],"approaches.":[143],"Finally,":[144],"economic":[145],"validation":[146],"through":[147],"backtesting":[148],"multi-factor":[151],"strategy":[153],"demonstrates":[154],"framework\u2019s":[156],"capacity":[157],"to":[158],"yield":[159],"actionable":[160],"insights":[161],"for":[162],"investors.":[163]},"counts_by_year":[],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2025-11-11T00:00:00"}