{"id":"https://openalex.org/W4414472021","doi":"https://doi.org/10.3390/systems13100839","title":"LLM-Driven Sentiment Analysis in MD&A: A Multi-Agent Framework for Corporate Misconduct Prediction","display_name":"LLM-Driven Sentiment Analysis in MD&A: A Multi-Agent Framework for Corporate Misconduct Prediction","publication_year":2025,"publication_date":"2025-09-24","ids":{"openalex":"https://openalex.org/W4414472021","doi":"https://doi.org/10.3390/systems13100839"},"language":"en","primary_location":{"id":"doi:10.3390/systems13100839","is_oa":true,"landing_page_url":"https://doi.org/10.3390/systems13100839","pdf_url":"https://www.mdpi.com/2079-8954/13/10/839/pdf?version=1758719361","source":{"id":"https://openalex.org/S4210219410","display_name":"Systems","issn_l":"2079-8954","issn":["2079-8954"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Systems","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2079-8954/13/10/839/pdf?version=1758719361","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100386685","display_name":"Yixin Liu","orcid":"https://orcid.org/0000-0002-9500-8333"},"institutions":[{"id":"https://openalex.org/I180726961","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70","country_code":"CN","type":"education","lineage":["https://openalex.org/I180726961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yeling Liu","raw_affiliation_strings":["College of Economics, Shenzhen University, Shenzhen 518055, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Economics, Shenzhen University, Shenzhen 518055, China","institution_ids":["https://openalex.org/I180726961"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086025068","display_name":"Yongkang Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I180726961","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70","country_code":"CN","type":"education","lineage":["https://openalex.org/I180726961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yongkang Liu","raw_affiliation_strings":["College of Economics, Shenzhen University, Shenzhen 518055, China"],"raw_orcid":"https://orcid.org/0009-0001-7380-681X","affiliations":[{"raw_affiliation_string":"College of Economics, Shenzhen University, Shenzhen 518055, China","institution_ids":["https://openalex.org/I180726961"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5037185687","display_name":"Kai Yang","orcid":"https://orcid.org/0000-0002-6371-7741"},"institutions":[{"id":"https://openalex.org/I180726961","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70","country_code":"CN","type":"education","lineage":["https://openalex.org/I180726961"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Kai Yang","raw_affiliation_strings":["College of Economics, Shenzhen University, Shenzhen 518055, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Economics, Shenzhen University, Shenzhen 518055, China","institution_ids":["https://openalex.org/I180726961"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5037185687"],"corresponding_institution_ids":["https://openalex.org/I180726961"],"apc_list":{"value":1600,"currency":"CHF","value_usd":1732},"apc_paid":{"value":1600,"currency":"CHF","value_usd":1732},"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.13032903,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"13","issue":"10","first_page":"839","last_page":"839"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9907000064849854,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9907000064849854,"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/T10260","display_name":"Software Engineering Research","score":0.9764000177383423,"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/T11644","display_name":"Spam and Phishing Detection","score":0.9574999809265137,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/sentiment-analysis","display_name":"Sentiment analysis","score":0.7106000185012817},{"id":"https://openalex.org/keywords/misconduct","display_name":"Misconduct","score":0.696399986743927},{"id":"https://openalex.org/keywords/lexicon","display_name":"Lexicon","score":0.6345999836921692},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4490000009536743},{"id":"https://openalex.org/keywords/empirical-research","display_name":"Empirical research","score":0.37770000100135803},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.3684000074863434},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.34599998593330383},{"id":"https://openalex.org/keywords/word-embedding","display_name":"Word embedding","score":0.34549999237060547}],"concepts":[{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.7106000185012817},{"id":"https://openalex.org/C2780587575","wikidata":"https://www.wikidata.org/wiki/Q6875295","display_name":"Misconduct","level":2,"score":0.696399986743927},{"id":"https://openalex.org/C2778121359","wikidata":"https://www.wikidata.org/wiki/Q8096","display_name":"Lexicon","level":2,"score":0.6345999836921692},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6140000224113464},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.47269999980926514},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4490000009536743},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.41029998660087585},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.37770000100135803},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.3684000074863434},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.34599998593330383},{"id":"https://openalex.org/C2777462759","wikidata":"https://www.wikidata.org/wiki/Q18395344","display_name":"Word embedding","level":3,"score":0.34549999237060547},{"id":"https://openalex.org/C155092808","wikidata":"https://www.wikidata.org/wiki/Q182557","display_name":"Computational linguistics","level":2,"score":0.33009999990463257},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.31779998540878296},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.31290000677108765},{"id":"https://openalex.org/C98501671","wikidata":"https://www.wikidata.org/wiki/Q1948408","display_name":"Text segmentation","level":3,"score":0.30250000953674316},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.301800012588501},{"id":"https://openalex.org/C2777267654","wikidata":"https://www.wikidata.org/wiki/Q3519023","display_name":"Test (biology)","level":2,"score":0.29919999837875366},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.2985999882221222},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.28610000014305115},{"id":"https://openalex.org/C171686336","wikidata":"https://www.wikidata.org/wiki/Q3532085","display_name":"Topic model","level":2,"score":0.2802000045776367},{"id":"https://openalex.org/C140331021","wikidata":"https://www.wikidata.org/wiki/Q1868104","display_name":"Logit","level":2,"score":0.2757999897003174},{"id":"https://openalex.org/C547195049","wikidata":"https://www.wikidata.org/wiki/Q1725664","display_name":"Terminology","level":2,"score":0.26159998774528503},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.2565999925136566}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.3390/systems13100839","is_oa":true,"landing_page_url":"https://doi.org/10.3390/systems13100839","pdf_url":"https://www.mdpi.com/2079-8954/13/10/839/pdf?version=1758719361","source":{"id":"https://openalex.org/S4210219410","display_name":"Systems","issn_l":"2079-8954","issn":["2079-8954"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Systems","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:a8d07d4a53804c3caee9ae638ccdf2d3","is_oa":true,"landing_page_url":"https://doaj.org/article/a8d07d4a53804c3caee9ae638ccdf2d3","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":"Systems, Vol 13, Iss 10, p 839 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3390/systems13100839","is_oa":true,"landing_page_url":"https://doi.org/10.3390/systems13100839","pdf_url":"https://www.mdpi.com/2079-8954/13/10/839/pdf?version=1758719361","source":{"id":"https://openalex.org/S4210219410","display_name":"Systems","issn_l":"2079-8954","issn":["2079-8954"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Systems","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G4418422222","display_name":null,"funder_award_id":"72401200","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5197067096","display_name":null,"funder_award_id":"72371168","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5295717513","display_name":null,"funder_award_id":"72472103","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320325571","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70"}],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4414472021.pdf"},"referenced_works_count":48,"referenced_works":["https://openalex.org/W216832231","https://openalex.org/W1966716734","https://openalex.org/W1976526581","https://openalex.org/W2072370348","https://openalex.org/W2102117374","https://openalex.org/W2103324604","https://openalex.org/W2105924481","https://openalex.org/W2121146822","https://openalex.org/W2147824299","https://openalex.org/W2148143831","https://openalex.org/W2162256707","https://openalex.org/W2164741508","https://openalex.org/W2268712472","https://openalex.org/W2293288130","https://openalex.org/W2313281002","https://openalex.org/W2315678552","https://openalex.org/W2468188327","https://openalex.org/W2554619162","https://openalex.org/W2610250061","https://openalex.org/W2625464253","https://openalex.org/W2752704646","https://openalex.org/W2895042943","https://openalex.org/W2979778574","https://openalex.org/W2996659323","https://openalex.org/W3026504503","https://openalex.org/W3122094160","https://openalex.org/W3122722586","https://openalex.org/W3123492911","https://openalex.org/W3123819747","https://openalex.org/W3125117705","https://openalex.org/W3125433491","https://openalex.org/W3125532177","https://openalex.org/W3125937743","https://openalex.org/W3125952890","https://openalex.org/W3151685851","https://openalex.org/W3153487575","https://openalex.org/W3166373724","https://openalex.org/W3184784506","https://openalex.org/W3192218804","https://openalex.org/W3209658145","https://openalex.org/W4224224556","https://openalex.org/W4248903981","https://openalex.org/W4253889179","https://openalex.org/W4313398738","https://openalex.org/W4319655570","https://openalex.org/W4394853018","https://openalex.org/W4400618774","https://openalex.org/W4408472883"],"related_works":[],"abstract_inverted_index":{"The":[0,104],"textual":[1],"analysis":[2,38,222],"of":[3,106],"Management":[4],"Discussion":[5],"and":[6,16,50,55,64,74,86,121,146,188,216,238],"Analysis":[7],"(MD&A)":[8],"reveals":[9],"valuable":[10],"insights":[11],"into":[12],"corporate":[13,101,172],"operational":[14],"performance":[15],"future":[17],"risks.":[18,244],"However,":[19],"techniques":[20],"for":[21,100,209,231],"accurately":[22],"extracting":[23],"sentiment":[24,37,123,166,183,221],"from":[25,124],"unstructured":[26],"Chinese":[27,52],"MD&A":[28,165],"texts":[29],"still":[30,79],"lack":[31],"comprehensiveness.":[32],"Existing":[33],"studies":[34],"related":[35],"to":[36,61,70,84,118,142,160,185],"often":[39],"use":[40],"lexicon-based":[41],"methods,":[42,128],"which":[43,112],"rely":[44],"on":[45,135],"predefined,":[46],"context-agnostic":[47],"word":[48,53],"lists":[49],"accurate":[51],"segmentation":[54],"struggle":[56],"with":[57,170],"domain-specific":[58],"terminology,":[59],"leading":[60],"limited":[62],"accuracy":[63],"interpretability.":[65],"Although":[66],"research":[67],"has":[68],"attempted":[69],"develop":[71],"context-aware":[72],"lexicons":[73],"language":[75],"models,":[76],"these":[77],"methods":[78],"face":[80],"limitations":[81],"when":[82],"applied":[83],"long":[85],"complex":[87],"financial":[88,148,220],"texts.":[89],"To":[90],"address":[91],"the":[92,163,194,218],"limitations,":[93],"we":[94],"propose":[95],"MDARisk,":[96],"a":[97,109,114,204,228],"novel":[98],"framework":[99,226],"misconduct":[102,210,243],"prediction.":[103],"core":[105],"MDARisk":[107,225],"is":[108,167],"MultiSenti":[110],"module,":[111],"leverages":[113],"multi-agent":[115],"LLM":[116],"approach":[117,202,230],"extract":[119],"comprehensive":[120],"contextual":[122],"MD&A.":[125],"Unlike":[126],"lexicon":[127],"our":[129,201],"LLM-based":[130],"module":[131],"interprets":[132],"words":[133],"based":[134],"their":[136],"surrounding":[137],"semantic":[138],"context,":[139],"allowing":[140],"it":[141],"decipher":[143],"nuanced":[144],"expressions":[145],"specialized":[147],"language.":[149],"We":[150,174],"first":[151],"conduct":[152],"an":[153],"econometric":[154],"validation":[155],"using":[156],"fixed-effects":[157],"logit":[158],"models":[159],"test":[161],"whether":[162],"MultiSenti-derived":[164],"significantly":[168],"associated":[169],"subsequent":[171],"misconduct.":[173],"then":[175],"evaluate":[176],"out-of-sample":[177],"predictive":[178,214],"utility":[179],"by":[180],"adding":[181],"this":[182],"feature":[184],"multiple":[186],"classifiers":[187],"assessing":[189,241],"its":[190],"incremental":[191],"gains":[192],"over":[193],"baseline":[195],"model.":[196],"Empirical":[197],"results":[198],"demonstrate":[199],"that":[200],"provides":[203,227],"more":[205],"reliable":[206],"sentiment-based":[207],"indicator":[208],"risk,":[211],"achieves":[212],"higher":[213],"accuracy,":[215],"outperforms":[217],"traditional":[219],"approach.":[223],"Our":[224],"cost-efficient":[229],"automated":[232],"disclosure":[233],"screening,":[234],"benefiting":[235],"auditors,":[236],"regulators,":[237],"investors":[239],"in":[240],"potential":[242]},"counts_by_year":[],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-09-25T00:00:00"}