{"id":"https://openalex.org/W3171504602","doi":"https://doi.org/10.1109/indin45582.2020.9442156","title":"An Entity-Level Sentiment Analysis of Financial Text Based on Pre-Trained Language Model","display_name":"An Entity-Level Sentiment Analysis of Financial Text Based on Pre-Trained Language Model","publication_year":2020,"publication_date":"2020-07-20","ids":{"openalex":"https://openalex.org/W3171504602","doi":"https://doi.org/10.1109/indin45582.2020.9442156","mag":"3171504602"},"language":"en","primary_location":{"id":"doi:10.1109/indin45582.2020.9442156","is_oa":false,"landing_page_url":"https://doi.org/10.1109/indin45582.2020.9442156","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE 18th International Conference on Industrial Informatics (INDIN)","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/A5084717091","display_name":"Zhihong Huang","orcid":"https://orcid.org/0000-0003-4569-5400"},"institutions":[{"id":"https://openalex.org/I4210125143","display_name":"Chengdu University","ror":"https://ror.org/034z67559","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210125143"]},{"id":"https://openalex.org/I24185976","display_name":"Sichuan University","ror":"https://ror.org/011ashp19","country_code":"CN","type":"education","lineage":["https://openalex.org/I24185976"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zhihong Huang","raw_affiliation_strings":["School of Computer Science, Sichuan University, Chengdu, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Sichuan University, Chengdu, China","institution_ids":["https://openalex.org/I4210125143","https://openalex.org/I24185976"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5017105526","display_name":"Zhijian Fang","orcid":null},"institutions":[{"id":"https://openalex.org/I3124059619","display_name":"China University of Geosciences","ror":"https://ror.org/04gcegc37","country_code":"CN","type":"education","lineage":["https://openalex.org/I3124059619"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhijian Fang","raw_affiliation_strings":["School of Automation, China University of Geosciences (Wuhan), Wuhan, China"],"affiliations":[{"raw_affiliation_string":"School of Automation, China University of Geosciences (Wuhan), Wuhan, China","institution_ids":["https://openalex.org/I3124059619"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5084717091"],"corresponding_institution_ids":["https://openalex.org/I24185976","https://openalex.org/I4210125143"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.2921642,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"391","last_page":"396"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9987999796867371,"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":0.9987999796867371,"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/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9987000226974487,"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/T13083","display_name":"Advanced Text Analysis Techniques","score":0.987500011920929,"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/computer-science","display_name":"Computer science","score":0.7094050049781799},{"id":"https://openalex.org/keywords/sentiment-analysis","display_name":"Sentiment analysis","score":0.7007932066917419},{"id":"https://openalex.org/keywords/the-internet","display_name":"The Internet","score":0.5217971801757812},{"id":"https://openalex.org/keywords/finance","display_name":"Finance","score":0.494396448135376},{"id":"https://openalex.org/keywords/sentence","display_name":"Sentence","score":0.4804229736328125},{"id":"https://openalex.org/keywords/financial-analysis","display_name":"Financial analysis","score":0.463803768157959},{"id":"https://openalex.org/keywords/financial-market","display_name":"Financial market","score":0.4616173207759857},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4505513310432434},{"id":"https://openalex.org/keywords/market-data","display_name":"Market data","score":0.42461958527565},{"id":"https://openalex.org/keywords/financial-ratio","display_name":"Financial ratio","score":0.4192190170288086},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.35498949885368347},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.3485935926437378},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.18155962228775024},{"id":"https://openalex.org/keywords/business","display_name":"Business","score":0.12760311365127563}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7094050049781799},{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.7007932066917419},{"id":"https://openalex.org/C110875604","wikidata":"https://www.wikidata.org/wiki/Q75","display_name":"The Internet","level":2,"score":0.5217971801757812},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.494396448135376},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.4804229736328125},{"id":"https://openalex.org/C130731218","wikidata":"https://www.wikidata.org/wiki/Q1363554","display_name":"Financial analysis","level":2,"score":0.463803768157959},{"id":"https://openalex.org/C19244329","wikidata":"https://www.wikidata.org/wiki/Q208697","display_name":"Financial market","level":2,"score":0.4616173207759857},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4505513310432434},{"id":"https://openalex.org/C114118609","wikidata":"https://www.wikidata.org/wiki/Q3036837","display_name":"Market data","level":2,"score":0.42461958527565},{"id":"https://openalex.org/C98014903","wikidata":"https://www.wikidata.org/wiki/Q832161","display_name":"Financial ratio","level":2,"score":0.4192190170288086},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.35498949885368347},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3485935926437378},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.18155962228775024},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.12760311365127563}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/indin45582.2020.9442156","is_oa":false,"landing_page_url":"https://doi.org/10.1109/indin45582.2020.9442156","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE 18th International Conference on Industrial Informatics (INDIN)","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":24,"referenced_works":["https://openalex.org/W202303397","https://openalex.org/W1211014924","https://openalex.org/W2079735306","https://openalex.org/W2112422413","https://openalex.org/W2124549482","https://openalex.org/W2135813353","https://openalex.org/W2136225293","https://openalex.org/W2149684865","https://openalex.org/W2160660844","https://openalex.org/W2166706824","https://openalex.org/W2171468534","https://openalex.org/W2270941958","https://openalex.org/W2297801999","https://openalex.org/W2464243336","https://openalex.org/W2584187309","https://openalex.org/W2949998441","https://openalex.org/W2951278869","https://openalex.org/W3124744263","https://openalex.org/W3125891473","https://openalex.org/W3210120707","https://openalex.org/W6608351394","https://openalex.org/W6627915109","https://openalex.org/W6763745640","https://openalex.org/W6764146914"],"related_works":["https://openalex.org/W2795754493","https://openalex.org/W2972972202","https://openalex.org/W2342804551","https://openalex.org/W3183649213","https://openalex.org/W3175836708","https://openalex.org/W2404695050","https://openalex.org/W3205883189","https://openalex.org/W2580331798","https://openalex.org/W4237967589","https://openalex.org/W167125014"],"abstract_inverted_index":{"With":[0],"the":[1,5,25,32,41,52,55,60,72,76,85,101,120,125,130,142,202],"rapid":[2],"development":[3],"of":[4,20,54,71,115,127,129,144,168],"Internet":[6],"and":[7,11,35,90,124,159,165],"global":[8],"finance,":[9],"more":[10,12],"financial":[13,27,42,56,73,102,116,131,148,169,199],"data":[14],"is":[15,31,138],"accumulated,":[16],"while":[17,189],"a":[18,155,160],"majority":[19],"them":[21],"are":[22,152],"unstructured.":[23],"Within":[24],"unstructured":[26],"data,":[28,171],"sentiment":[29,45,69,79,113,149],"information":[30,98,204],"most":[33],"attractive":[34],"commonly":[36],"desired":[37],"to":[38,50,65,105,140],"fetch":[39],"for":[40,185,197],"participants,":[43],"because":[44],"can":[46,62,182,194],"be":[47,63,183,195],"an":[48],"indicator":[49],"reflect":[51],"state":[53],"market,":[57],"which":[58],"means":[59],"market":[61],"predicted":[64],"some":[66],"extent":[67],"through":[68],"analysis":[70,80,114],"text":[74,117],"on":[75,99,119],"Internet.":[77],"Although":[78],"has":[81],"been":[82],"studied":[83],"at":[84,111,178,190],"document":[86],"level,":[87,89,92],"sentence":[88],"aspect":[91],"it":[93],"may":[94],"not":[95],"provide":[96],"enough":[97],"what":[100],"participants":[103],"want":[104],"know.":[106],"Therefore,":[107],"this":[108],"paper":[109],"aims":[110],"entity-level":[112],"based":[118],"pre-trained":[121,145],"language":[122,146],"model":[123],"improvement":[126],"granularity":[128],"information.":[132],"Bidirectional":[133],"Encoder":[134],"Representations":[135],"from":[136],"Transformers":[137],"adopted":[139],"illustrate":[141],"efficacy":[143],"model-based":[147],"analysis.":[150],"Experiments":[151],"conducted":[153],"with":[154,163],"labeled":[156],"test":[157],"set":[158,162],"train":[161],"8,000":[164],"10,000":[166],"pieces":[167],"network":[170],"respectively.":[172],"The":[173],"experiment":[174],"results":[175],"show":[176],"that":[177,201],"least":[179,191],"95%":[180],"accuracy":[181,193],"achieved":[184,196],"determining":[186],"negative":[187,203],"information,":[188],"93%":[192],"fetching":[198],"entity":[200],"correlates":[205],"to.":[206]},"counts_by_year":[{"year":2025,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
