{"id":"https://openalex.org/W4391250870","doi":"https://doi.org/10.1109/coginfocom59411.2023.10397544","title":"Language of the Market: NLP-Driven Sentiment Analysis of Hungarian Economy","display_name":"Language of the Market: NLP-Driven Sentiment Analysis of Hungarian Economy","publication_year":2023,"publication_date":"2023-09-22","ids":{"openalex":"https://openalex.org/W4391250870","doi":"https://doi.org/10.1109/coginfocom59411.2023.10397544"},"language":"en","primary_location":{"id":"doi:10.1109/coginfocom59411.2023.10397544","is_oa":false,"landing_page_url":"https://doi.org/10.1109/coginfocom59411.2023.10397544","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 14th IEEE International Conference on Cognitive Infocommunications (CogInfoCom)","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/A5033012022","display_name":"Frigyes Viktor Arthur","orcid":"https://orcid.org/0000-0003-2125-4344"},"institutions":[{"id":"https://openalex.org/I29770179","display_name":"Budapest University of Technology and Economics","ror":"https://ror.org/02w42ss30","country_code":"HU","type":"education","lineage":["https://openalex.org/I29770179"]}],"countries":["HU"],"is_corresponding":true,"raw_author_name":"Frigyes Viktor Arthur","raw_affiliation_strings":["Budapest University of Technology and Economics,Department of Telecommunications and Media Informatics,Budapest,Hungary","Department of Telecommunications and Media Informatics, Budapest University of Technology and Economics, Budapest, Hungary"],"affiliations":[{"raw_affiliation_string":"Budapest University of Technology and Economics,Department of Telecommunications and Media Informatics,Budapest,Hungary","institution_ids":["https://openalex.org/I29770179"]},{"raw_affiliation_string":"Department of Telecommunications and Media Informatics, Budapest University of Technology and Economics, Budapest, Hungary","institution_ids":["https://openalex.org/I29770179"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018247113","display_name":"B\u00e1lint Gyires-T\u00f3th","orcid":"https://orcid.org/0000-0003-1059-9822"},"institutions":[{"id":"https://openalex.org/I29770179","display_name":"Budapest University of Technology and Economics","ror":"https://ror.org/02w42ss30","country_code":"HU","type":"education","lineage":["https://openalex.org/I29770179"]}],"countries":["HU"],"is_corresponding":false,"raw_author_name":"B\u00e1lint Gyires-T\u00f3th","raw_affiliation_strings":["Budapest University of Technology and Economics,Department of Telecommunications and Media Informatics,Budapest,Hungary","Department of Telecommunications and Media Informatics, Budapest University of Technology and Economics, Budapest, Hungary"],"affiliations":[{"raw_affiliation_string":"Budapest University of Technology and Economics,Department of Telecommunications and Media Informatics,Budapest,Hungary","institution_ids":["https://openalex.org/I29770179"]},{"raw_affiliation_string":"Department of Telecommunications and Media Informatics, Budapest University of Technology and Economics, Budapest, Hungary","institution_ids":["https://openalex.org/I29770179"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5093794583","display_name":"M\u00e1t\u00e9 Imre Debreczeni","orcid":null},"institutions":[{"id":"https://openalex.org/I162147314","display_name":"Hungarian National Bank","ror":"https://ror.org/02awmn551","country_code":"HU","type":"other","lineage":["https://openalex.org/I162147314"]}],"countries":["HU"],"is_corresponding":false,"raw_author_name":"M\u00e1t\u00e9 Imre Debreczeni","raw_affiliation_strings":["Central Bank of Hungary,Digitalization Technology Department,Budapest,Hungary","Digitalization Technology Department, Central Bank of Hungary, Budapest, Hungary"],"affiliations":[{"raw_affiliation_string":"Central Bank of Hungary,Digitalization Technology Department,Budapest,Hungary","institution_ids":["https://openalex.org/I162147314"]},{"raw_affiliation_string":"Digitalization Technology Department, Central Bank of Hungary, Budapest, Hungary","institution_ids":["https://openalex.org/I162147314"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5093794584","display_name":"L\u00edvia R\u00e9ka \u00d3noz\u00f3","orcid":null},"institutions":[{"id":"https://openalex.org/I29770179","display_name":"Budapest University of Technology and Economics","ror":"https://ror.org/02w42ss30","country_code":"HU","type":"education","lineage":["https://openalex.org/I29770179"]},{"id":"https://openalex.org/I162147314","display_name":"Hungarian National Bank","ror":"https://ror.org/02awmn551","country_code":"HU","type":"other","lineage":["https://openalex.org/I162147314"]}],"countries":["HU"],"is_corresponding":false,"raw_author_name":"L\u00edvia R\u00e9ka \u00d3noz\u00f3","raw_affiliation_strings":["Budapest University of Technology and Economics,Department of Telecommunications and Media Informatics,Budapest,Hungary","Department of Telecommunications and Media Informatics, Budapest University of Technology and Economics, Budapest, Hungary","Digitalization Technology Department, Central Bank of Hungary, Budapest, Hungary"],"affiliations":[{"raw_affiliation_string":"Budapest University of Technology and Economics,Department of Telecommunications and Media Informatics,Budapest,Hungary","institution_ids":["https://openalex.org/I29770179"]},{"raw_affiliation_string":"Department of Telecommunications and Media Informatics, Budapest University of Technology and Economics, Budapest, Hungary","institution_ids":["https://openalex.org/I29770179"]},{"raw_affiliation_string":"Digitalization Technology Department, Central Bank of Hungary, Budapest, Hungary","institution_ids":["https://openalex.org/I162147314"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5033012022"],"corresponding_institution_ids":["https://openalex.org/I29770179"],"apc_list":null,"apc_paid":null,"fwci":0.3497,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.67967643,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"000093","last_page":"000098"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.993399977684021,"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/T10028","display_name":"Topic Modeling","score":0.993399977684021,"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":0.9854000210762024,"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/T13910","display_name":"Computational and Text Analysis Methods","score":0.9733999967575073,"subfield":{"id":"https://openalex.org/subfields/3300","display_name":"General Social Sciences"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/sentiment-analysis","display_name":"Sentiment analysis","score":0.8492399454116821},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7441344857215881},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6616250872612},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.4894489347934723},{"id":"https://openalex.org/keywords/stock-market","display_name":"Stock market","score":0.45978429913520813},{"id":"https://openalex.org/keywords/cognition","display_name":"Cognition","score":0.4585009515285492},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.43253618478775024},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3730999231338501},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.08191311359405518},{"id":"https://openalex.org/keywords/history","display_name":"History","score":0.081144779920578}],"concepts":[{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.8492399454116821},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7441344857215881},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6616250872612},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.4894489347934723},{"id":"https://openalex.org/C2780299701","wikidata":"https://www.wikidata.org/wiki/Q475000","display_name":"Stock market","level":3,"score":0.45978429913520813},{"id":"https://openalex.org/C169900460","wikidata":"https://www.wikidata.org/wiki/Q2200417","display_name":"Cognition","level":2,"score":0.4585009515285492},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.43253618478775024},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3730999231338501},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.08191311359405518},{"id":"https://openalex.org/C95457728","wikidata":"https://www.wikidata.org/wiki/Q309","display_name":"History","level":0,"score":0.081144779920578},{"id":"https://openalex.org/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","level":1,"score":0.0},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/coginfocom59411.2023.10397544","is_oa":false,"landing_page_url":"https://doi.org/10.1109/coginfocom59411.2023.10397544","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 14th IEEE International Conference on Cognitive Infocommunications (CogInfoCom)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Decent work and economic growth","id":"https://metadata.un.org/sdg/8","score":0.4000000059604645}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W1971415293","https://openalex.org/W2058233873","https://openalex.org/W2493916176","https://openalex.org/W2554619162","https://openalex.org/W2965373594","https://openalex.org/W3001279689","https://openalex.org/W3122648113","https://openalex.org/W3124174211","https://openalex.org/W3139995863","https://openalex.org/W4385245566","https://openalex.org/W6682691769"],"related_works":["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","https://openalex.org/W4317653575"],"abstract_inverted_index":{"This":[0],"paper":[1],"introduces":[2],"deep":[3],"learning-based":[4],"natural":[5],"language":[6],"processing":[7],"approach":[8],"to":[9,22,35],"correlate":[10],"sentiment":[11,58,74,86,111,130,136],"trajectory":[12,141],"derived":[13],"from":[14],"business":[15,65],"news":[16,96],"with":[17,113,133],"macroeconomic":[18,114,145],"indicators,":[19],"thereby":[20],"contributing":[21],"a":[23,43,60,99,104],"low-latency":[24],"and":[25,49,78,97,119],"nuanced":[26],"understanding":[27],"of":[28,38,59,64,95,123,142],"economic":[29],"trends.":[30],"Thus,":[31],"our":[32],"work":[33],"contributes":[34],"the":[36,57,84,109,128,134,140,143],"field":[37],"cognitive":[39,47,52],"infocommunications":[40],"by":[41],"establishing":[42],"connection":[44],"between":[45],"human":[46],"processes":[48],"an":[50],"artificially":[51],"system.":[53],"First":[54],"we":[55,88,107],"labeled":[56],"relatively":[61],"small":[62],"number":[63],"related":[66],"sentences.":[67],"We":[68],"then":[69],"trained":[70,85],"neural":[71],"networks":[72],"for":[73,91],"classification.":[75],"Generally,":[76],"FastText-":[77],"transfomer-based":[79],"approaches":[80],"were":[81],"investigated.":[82],"Using":[83],"classifiers,":[87],"made":[89],"predictions":[90],"over":[92],"thirty":[93],"years":[94],"introduce":[98],"monthly":[100],"aggregation":[101,137],"method.":[102],"As":[103],"final":[105],"step,":[106],"compare":[108],"predictive":[110],"trajectories":[112],"indicators":[115],"such":[116],"as":[117],"GDP":[118],"PMI.":[120],"The":[121],"results":[122],"this":[124],"study":[125],"indicate":[126],"that":[127],"transformer-based":[129],"classifier":[131],"along":[132],"proposed":[135],"method":[138],"follows":[139],"inspected":[144],"indicators.":[146]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}