{"id":"https://openalex.org/W7140143957","doi":"https://doi.org/10.18653/v1/2026.eacl-long.129","title":"ViGoEmotions: A Benchmark Dataset For Fine-grained Emotion Detection on Vietnamese Texts","display_name":"ViGoEmotions: A Benchmark Dataset For Fine-grained Emotion Detection on Vietnamese Texts","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7140143957","doi":"https://doi.org/10.18653/v1/2026.eacl-long.129"},"language":null,"primary_location":{"id":"doi:10.18653/v1/2026.eacl-long.129","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.eacl-long.129","pdf_url":"https://aclanthology.org/2026.eacl-long.129.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://aclanthology.org/2026.eacl-long.129.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5130355186","display_name":"Tran Quang Hung","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tran Quang Hung","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130389859","display_name":"Pham Tien Nam","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pham Tien Nam","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069724988","display_name":"Son T. Luu","orcid":"https://orcid.org/0000-0002-1231-5865"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Son T. Luu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5130399405","display_name":"Kiet Van Nguyen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kiet Van Nguyen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.38990796,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"2805","last_page":"2831"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10667","display_name":"Emotion and Mood Recognition","score":0.7982000112533569,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T10667","display_name":"Emotion and Mood Recognition","score":0.7982000112533569,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.15199999511241913,"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/T12313","display_name":"Emotions and Moral Behavior","score":0.005200000014156103,"subfield":{"id":"https://openalex.org/subfields/3207","display_name":"Social Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/vietnamese","display_name":"Vietnamese","score":0.6801000237464905},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5206000208854675},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.27459999918937683},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.2567000091075897}],"concepts":[{"id":"https://openalex.org/C103621254","wikidata":"https://www.wikidata.org/wiki/Q9199","display_name":"Vietnamese","level":2,"score":0.6801000237464905},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6209999918937683},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6129999756813049},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.554099977016449},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5206000208854675},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.30160000920295715},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.28999999165534973},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.27459999918937683},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.259799987077713},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2567000091075897},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.25110000371932983}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.18653/v1/2026.eacl-long.129","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.eacl-long.129","pdf_url":"https://aclanthology.org/2026.eacl-long.129.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2602.08371","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2602.08371","pdf_url":"https://arxiv.org/pdf/2602.08371","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"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-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"doi:10.18653/v1/2026.eacl-long.129","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.eacl-long.129","pdf_url":"https://aclanthology.org/2026.eacl-long.129.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7140143957.pdf","grobid_xml":"https://content.openalex.org/works/W7140143957.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Emotion":[0],"classification":[1],"plays":[2],"a":[3,88],"significant":[4],"role":[5],"in":[6,14,26,41],"emotion":[7,34,62],"prediction":[8],"and":[9,58,85,116,133,144,160],"harmful":[10],"content":[11],"detection.Recent":[12],"advancements":[13],"NLP,":[15],"particularly":[16],"through":[17],"large":[18],"language":[19],"models":[20,67],"(LLMs),":[21],"have":[22],"greatly":[23],"improved":[24],"outcomes":[25],"this":[27],"field.This":[28],"study":[29],"introduces":[30],"ViGoEmotions":[31],"-a":[32],"Vietnamese":[33],"corpus":[35,152],"comprising":[36],"20,664":[37],"social":[38],"media":[39],"comments":[40],"which":[42],"each":[43],"comment":[44],"is":[45],"classified":[46],"into":[47,82,97],"27":[48],"fine-grained":[49],"distinct":[50],"emotions.To":[51],"evaluate":[52],"the":[53,56,101,111,127,150],"quality":[54,162],"of":[55,103,131,136],"dataset":[57],"its":[59],"impact":[60],"on":[61],"classification,":[63],"eight":[64],"pretrained":[65],"Transformer-based":[66],"were":[68],"evaluated":[69],"under":[70],"three":[71],"preprocessing":[72,158],"strategies:":[73],"preserving":[74,108],"original":[75],"emojis":[76,81,96,109,120],"with":[77],"rule-based":[78],"normalization,":[79],"converting":[80,95],"textual":[83],"descriptions,":[84],"applying":[86],"ViSoLex,":[87],"modelbased":[89],"lexical":[90],"normalization":[91],"system.Results":[92],"show":[93],"that":[94,148],"text":[98],"often":[99],"improves":[100],"performance":[102,138],"several":[104],"BERT-based":[105],"baselines,":[106],"while":[107,149],"yields":[110],"best":[112],"results":[113],"for":[114],"ViSoBERT":[115],"CafeBERT.In":[117],"contrast,":[118],"removing":[119],"generally":[121],"leads":[122],"to":[123],"lower":[124],"performance.ViSoBERT":[125],"achieved":[126],"highest":[128],"Macro":[129],"F1-score":[130,135],"61.50%":[132],"Weighted":[134],"63.26%.Strong":[137],"was":[139],"also":[140],"observed":[141],"from":[142],"CafeBERT":[143],"PhoBERT.These":[145],"findings":[146],"highlight":[147],"proposed":[151],"can":[153],"support":[154],"diverse":[155],"architectures":[156],"effectively,":[157],"strategies":[159],"annotation":[161],"remain":[163],"key":[164],"factors":[165],"influencing":[166],"downstream":[167],"performance.":[168]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-02-12T00:00:00"}