{"id":"https://openalex.org/W4406458372","doi":"https://doi.org/10.1109/bigdata62323.2024.10825946","title":"Stock Price Prediction Using LLM-Based Sentiment Analysis","display_name":"Stock Price Prediction Using LLM-Based Sentiment Analysis","publication_year":2024,"publication_date":"2024-12-15","ids":{"openalex":"https://openalex.org/W4406458372","doi":"https://doi.org/10.1109/bigdata62323.2024.10825946"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata62323.2024.10825946","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata62323.2024.10825946","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Big Data (BigData)","raw_type":"proceedings-article"},"type":"article","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/A5017181882","display_name":"Qizhao Chen","orcid":null},"institutions":[{"id":"https://openalex.org/I180941496","display_name":"University of Hyogo","ror":"https://ror.org/0151bmh98","country_code":"JP","type":"education","lineage":["https://openalex.org/I180941496"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Qizhao Chen","raw_affiliation_strings":["University of Hyogo,Graduate School of Information Science,Kobe,Japan"],"affiliations":[{"raw_affiliation_string":"University of Hyogo,Graduate School of Information Science,Kobe,Japan","institution_ids":["https://openalex.org/I180941496"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5004107850","display_name":"Hiroaki Kawashima","orcid":"https://orcid.org/0000-0003-1208-3715"},"institutions":[{"id":"https://openalex.org/I180941496","display_name":"University of Hyogo","ror":"https://ror.org/0151bmh98","country_code":"JP","type":"education","lineage":["https://openalex.org/I180941496"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Hiroaki Kawashima","raw_affiliation_strings":["University of Hyogo,Graduate School of Information Science,Kobe,Japan"],"affiliations":[{"raw_affiliation_string":"University of Hyogo,Graduate School of Information Science,Kobe,Japan","institution_ids":["https://openalex.org/I180941496"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5017181882"],"corresponding_institution_ids":["https://openalex.org/I180941496"],"apc_list":null,"apc_paid":null,"fwci":8.5085,"has_fulltext":false,"cited_by_count":17,"citation_normalized_percentile":{"value":0.97903747,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"4846","last_page":"4853"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9998000264167786,"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":0.9998000264167786,"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/T11052","display_name":"Energy Load and Power Forecasting","score":0.9865999817848206,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11918","display_name":"Forecasting Techniques and Applications","score":0.9818999767303467,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/stock-price","display_name":"Stock price","score":0.6003791689872742},{"id":"https://openalex.org/keywords/stock","display_name":"Stock (firearms)","score":0.5836912393569946},{"id":"https://openalex.org/keywords/sentiment-analysis","display_name":"Sentiment analysis","score":0.5790875554084778},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5556839108467102},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.40514278411865234},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.33045053482055664},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.16540342569351196},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.07591360807418823},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.061713725328445435},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.05708155035972595}],"concepts":[{"id":"https://openalex.org/C2988984586","wikidata":"https://www.wikidata.org/wiki/Q1020013","display_name":"Stock price","level":3,"score":0.6003791689872742},{"id":"https://openalex.org/C204036174","wikidata":"https://www.wikidata.org/wiki/Q909380","display_name":"Stock (firearms)","level":2,"score":0.5836912393569946},{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.5790875554084778},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5556839108467102},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.40514278411865234},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.33045053482055664},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.16540342569351196},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.07591360807418823},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.061713725328445435},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.05708155035972595},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"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/bigdata62323.2024.10825946","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata62323.2024.10825946","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Big Data (BigData)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/8","score":0.4099999964237213,"display_name":"Decent work and economic growth"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":39,"referenced_works":["https://openalex.org/W1546425147","https://openalex.org/W2099813784","https://openalex.org/W2510642588","https://openalex.org/W2770795498","https://openalex.org/W2792764867","https://openalex.org/W2799635155","https://openalex.org/W2884322849","https://openalex.org/W2899992493","https://openalex.org/W2949676527","https://openalex.org/W2977178908","https://openalex.org/W3041125167","https://openalex.org/W3130456109","https://openalex.org/W3171038109","https://openalex.org/W3177318507","https://openalex.org/W3193616934","https://openalex.org/W4205627296","https://openalex.org/W4224116415","https://openalex.org/W4225926372","https://openalex.org/W4281675547","https://openalex.org/W4300485781","https://openalex.org/W4320016125","https://openalex.org/W4327969336","https://openalex.org/W4377292358","https://openalex.org/W4385243642","https://openalex.org/W4385245566","https://openalex.org/W4385539737","https://openalex.org/W4386891295","https://openalex.org/W4390305662","https://openalex.org/W4390788722","https://openalex.org/W4391512547","https://openalex.org/W6666761814","https://openalex.org/W6739901393","https://openalex.org/W6749825310","https://openalex.org/W6767182473","https://openalex.org/W6810276077","https://openalex.org/W6810458839","https://openalex.org/W6853399361","https://openalex.org/W6861026998","https://openalex.org/W6862675204"],"related_works":["https://openalex.org/W247222457","https://openalex.org/W1488120909","https://openalex.org/W3124131549","https://openalex.org/W2152348935","https://openalex.org/W2887069341","https://openalex.org/W2554106722","https://openalex.org/W1797892342","https://openalex.org/W4240248738","https://openalex.org/W3008476150","https://openalex.org/W2093710055"],"abstract_inverted_index":{"This":[0],"paper":[1],"examines":[2],"the":[3,18,72,83,96,100,117,133,149,156,165,177],"effectiveness":[4],"of":[5,20,99,136,155,173],"recent":[6,61],"large":[7,62],"language":[8,63],"model-based":[9],"news":[10,53,97,137,143],"sentiment":[11,31,54,79,144],"estimation":[12],"for":[13,160],"stock":[14,118,150],"price":[15,151],"forecasting":[16],"with":[17,88,120,126],"combination":[19],"latest":[21],"transformer-based":[22],"prediction":[23,134,162],"models.":[24],"To":[25],"achieve":[26],"a":[27],"better":[28],"accuracy":[29],"in":[30,51,77,168,185],"classification,":[32,55],"experiments":[33],"are":[34,71,113,129],"designed":[35,130],"to":[36,94,115,131],"compare":[37],"six":[38],"different":[39,121,127],"models":[40,64,76,159],"(GPT":[41],"4,":[42],"Llama":[43,86],"3,":[44,87],"Gemma":[45],"2,":[46],"Mistral":[47],"7b,":[48],"FinBERT,":[49],"VADER)":[50],"financial":[52,78],"and":[56,68,110],"it":[57],"was":[58],"found":[59],"that":[60,141,176],"can":[65,146],"outperform":[66],"FinBERT":[67],"VADER,":[69],"which":[70],"most":[73,169],"commonly":[74],"used":[75,114],"analysis.":[80],"Based":[81],"on":[82],"experiment":[84],"results,":[85],"relatively":[89],"stable":[90],"performance,":[91],"is":[92],"chosen":[93],"classify":[95],"sentiments":[98],"selected":[101],"companies.":[102],"Informer,":[103,153],"Transformer,":[104],"TCN,":[105],"LSTM,":[106],"SVR,":[107],"Random":[108],"Forest":[109],"Naive":[111],"Forecast":[112],"predict":[116],"prices":[119],"sliding":[122],"window":[123],"sizes.":[124],"Experiments":[125],"scenarios":[128],"evaluate":[132],"ability":[135],"sentiment.":[138],"Results":[139],"show":[140],"adding":[142],"data":[145],"indeed":[147],"improve":[148],"prediction.":[152],"one":[154],"state-of-the-art":[157],"transformer":[158],"long-term":[161],"tasks,":[163],"yields":[164],"best":[166],"performances":[167],"cases.":[170],"Ablation":[171],"study":[172],"Informer":[174],"suggests":[175],"generative":[178],"style":[179],"decoder":[180],"plays":[181],"an":[182],"important":[183],"role":[184],"performance":[186],"improvement.":[187]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":15}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}