{"id":"https://openalex.org/W4408441399","doi":"https://doi.org/10.1109/access.2025.3551228","title":"Using Deep Learning Transformers for Detection of Hedonic Emotional States by Analyzing Eudaimonic Behavior of Online Users","display_name":"Using Deep Learning Transformers for Detection of Hedonic Emotional States by Analyzing Eudaimonic Behavior of Online Users","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4408441399","doi":"https://doi.org/10.1109/access.2025.3551228"},"language":"en","primary_location":{"id":"doi:10.1109/access.2025.3551228","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3551228","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.2025.3551228","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5116622236","display_name":"Cailian Qu","orcid":null},"institutions":[{"id":"https://openalex.org/I18452120","display_name":"Yantai University","ror":"https://ror.org/01rp41m56","country_code":"CN","type":"education","lineage":["https://openalex.org/I18452120"]},{"id":"https://openalex.org/I4210102741","display_name":"Jiangsu Vocational College of Medicine","ror":"https://ror.org/01jzst437","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210102741"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Cailian Qu","raw_affiliation_strings":["Student Affairs Office (Youth League Committee), Yantai Vocational College, Yantai, China","Student Affairs Office(Youth League Committee), Yantai Vocational College, Yantai, China"],"raw_orcid":"https://orcid.org/0009-0001-4825-3731","affiliations":[{"raw_affiliation_string":"Student Affairs Office (Youth League Committee), Yantai Vocational College, Yantai, China","institution_ids":["https://openalex.org/I18452120"]},{"raw_affiliation_string":"Student Affairs Office(Youth League Committee), Yantai Vocational College, Yantai, China","institution_ids":["https://openalex.org/I4210102741"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5030578285","display_name":"Yang Zheng-li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhengli Yang","raw_affiliation_strings":["Intelligent Manufacturing Department, Yantai Vocational College, Yantai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Intelligent Manufacturing Department, Yantai Vocational College, Yantai, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":2.792,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.90003085,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":97},"biblio":{"volume":"13","issue":null,"first_page":"50931","last_page":"50952"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.2134000062942505,"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/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.2134000062942505,"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":0.587070643901825},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.42577242851257324},{"id":"https://openalex.org/keywords/online-learning","display_name":"Online learning","score":0.4237349033355713},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3668278455734253},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3327457904815674},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.32394877076148987},{"id":"https://openalex.org/keywords/multimedia","display_name":"Multimedia","score":0.18886804580688477},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.08429482579231262}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.587070643901825},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.42577242851257324},{"id":"https://openalex.org/C2986087404","wikidata":"https://www.wikidata.org/wiki/Q15946010","display_name":"Online learning","level":2,"score":0.4237349033355713},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3668278455734253},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3327457904815674},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.32394877076148987},{"id":"https://openalex.org/C49774154","wikidata":"https://www.wikidata.org/wiki/Q131765","display_name":"Multimedia","level":1,"score":0.18886804580688477},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.08429482579231262},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2025.3551228","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3551228","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:6af6f153deba4fd789d310c3bb8a9a3c","is_oa":true,"landing_page_url":"https://doaj.org/article/6af6f153deba4fd789d310c3bb8a9a3c","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 50931-50952 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2025.3551228","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3551228","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":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.5199999809265137}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":54,"referenced_works":["https://openalex.org/W2050685090","https://openalex.org/W2428408384","https://openalex.org/W2586115578","https://openalex.org/W2918378401","https://openalex.org/W2944459080","https://openalex.org/W3010627298","https://openalex.org/W3027527029","https://openalex.org/W3034008867","https://openalex.org/W3090154305","https://openalex.org/W3090939438","https://openalex.org/W3097693500","https://openalex.org/W3125992765","https://openalex.org/W3127917488","https://openalex.org/W3131043787","https://openalex.org/W3131261667","https://openalex.org/W3173046598","https://openalex.org/W3176130152","https://openalex.org/W3180621975","https://openalex.org/W3212411386","https://openalex.org/W4200174246","https://openalex.org/W4200428630","https://openalex.org/W4210363348","https://openalex.org/W4220903755","https://openalex.org/W4283021117","https://openalex.org/W4284695904","https://openalex.org/W4285077244","https://openalex.org/W4285742398","https://openalex.org/W4296638422","https://openalex.org/W4301185403","https://openalex.org/W4311490074","https://openalex.org/W4313294355","https://openalex.org/W4315620857","https://openalex.org/W4362638934","https://openalex.org/W4367325708","https://openalex.org/W4379053041","https://openalex.org/W4380887019","https://openalex.org/W4386410024","https://openalex.org/W4386541786","https://openalex.org/W4386931178","https://openalex.org/W4387882247","https://openalex.org/W4388400891","https://openalex.org/W4388551649","https://openalex.org/W4389749974","https://openalex.org/W4390488555","https://openalex.org/W4392249786","https://openalex.org/W4401073211","https://openalex.org/W4401820675","https://openalex.org/W4402727048","https://openalex.org/W4403137838","https://openalex.org/W4403759060","https://openalex.org/W4404031077","https://openalex.org/W4404902220","https://openalex.org/W4406081181","https://openalex.org/W6761643289"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W4387369504","https://openalex.org/W3046775127","https://openalex.org/W4394896187","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W3107602296","https://openalex.org/W4364306694","https://openalex.org/W4312192474"],"abstract_inverted_index":{"Hedonic":[0],"emotions":[1,21,75,183],"represent":[2],"a":[3,23,59,174,242],"significant":[4],"concept":[5],"in":[6,26,30,68,191,249],"the":[7,12,34,104,159,179,188,192,206,224,234],"study":[8],"of":[9,14,37,62,72,91,101,118,181,198,209,246],"linguistics,":[10],"encapsulates":[11],"patterns":[13,180,227],"positivity,":[15],"pleasures,":[16],"activities,":[17],"and":[18,42,54,65,87,124,147,184,222],"enjoyment.":[19],"These":[20],"play":[22],"critical":[24],"role":[25],"shaping":[27],"eudaimonic":[28],"behavior":[29,49,190],"that":[31,56],"users":[32],"reflect":[33],"personal":[35,66],"growth":[36,67],"individual,":[38],"personality,":[39],"social":[40],"connections,":[41],"values,":[43],"leading":[44],"to":[45,51,58,107,186,219,241],"positive":[46,182],"psychology.":[47],"This":[48,195],"refers":[50],"taking":[52],"actions":[53],"practices":[55],"contribute":[57],"deep":[60],"sense":[61],"purpose,":[63],"meaning,":[64],"life.":[69],"The":[70,89,167],"significance":[71],"detecting":[73],"these":[74],"extends":[76],"beyond":[77],"psychological":[78],"well-being,":[79],"impacting":[80],"fields":[81],"such":[82,138,150],"as":[83,139,151],"marketing,":[84],"mental":[85],"health,":[86],"education.":[88],"area":[90],"hedonic":[92,102,225],"emotion":[93,226],"remains":[94],"underexplored.":[95],"In":[96,157],"this":[97],"study,":[98],"for":[99],"classification":[100],"emotions,":[103],"main":[105],"aim":[106],"carry":[108],"out":[109,172],"an":[110],"extensive":[111],"empirical":[112,196],"analysis":[113,197],"by":[114,177],"applying":[115],"computational":[116],"models":[117],"shallow":[119],"machine":[120],"learning,":[121,123],"ensemble":[122],"advanced":[125,216],"Deep":[126],"Learning":[127],"(DL)":[128],"algorithms.":[129],"Moreover,":[130],"diverse":[131],"feature":[132],"extraction":[133],"techniques":[134],"including":[135],"textual":[136,230],"features":[137],"Term":[140],"Frequency-Inverse":[141],"Document":[142],"Frequency":[143],"(TF-IDF),":[144],"Part-of-Speech":[145],"(PoS)":[146],"word":[148],"embeddings":[149,162],"word2vec,":[152],"GloVe":[153],"will":[154],"be":[155],"explored.":[156],"addition,":[158],"state-of-the-art":[160],"sentence":[161],"have":[163],"also":[164],"been":[165,170],"computed.":[166],"experimentation":[168],"has":[169],"carried":[171],"on":[173],"standard":[175,202],"dataset":[176],"analyzing":[178],"activities":[185],"understand":[187],"user\u2019s":[189],"digital":[193,250],"world.":[194],"results":[199],"measured":[200],"with":[201,211],"performance":[203],"measures":[204],"reveals":[205],"highest":[207],"accuracy":[208],"97%":[210],"transformer-based":[212],"model":[213],"RoBERTa":[214],"using":[215],"embedding":[217],"approach,":[218],"effectively":[220],"detect":[221],"identify":[223],"from":[228],"online":[229],"content.":[231],"By":[232],"bridging":[233],"existing":[235],"research":[236,239],"gap,":[237],"our":[238],"contributes":[240],"more":[243],"comprehensive":[244],"understanding":[245],"emotional":[247],"dynamics":[248],"interactions.":[251]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1}],"updated_date":"2025-12-28T23:10:05.387466","created_date":"2025-10-10T00:00:00"}