{"id":"https://openalex.org/W4414597476","doi":"https://doi.org/10.1186/s40537-025-01276-6","title":"Label knowledge-guided heterogeneous graph contrastive learning for semi-supervised short text sentiment classification","display_name":"Label knowledge-guided heterogeneous graph contrastive learning for semi-supervised short text sentiment classification","publication_year":2025,"publication_date":"2025-09-29","ids":{"openalex":"https://openalex.org/W4414597476","doi":"https://doi.org/10.1186/s40537-025-01276-6"},"language":"en","primary_location":{"id":"doi:10.1186/s40537-025-01276-6","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s40537-025-01276-6","pdf_url":"https://journalofbigdata.springeropen.com/counter/pdf/10.1186/s40537-025-01276-6","source":{"id":"https://openalex.org/S2737955091","display_name":"Journal Of Big Data","issn_l":"2196-1115","issn":["2196-1115"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Big Data","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://journalofbigdata.springeropen.com/counter/pdf/10.1186/s40537-025-01276-6","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5003137580","display_name":"Mingqiang Wu","orcid":"https://orcid.org/0000-0003-3179-028X"},"institutions":[{"id":"https://openalex.org/I196038209","display_name":"Hunan University of Arts and Science","ror":"https://ror.org/01ggnn306","country_code":"CN","type":"education","lineage":["https://openalex.org/I196038209"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Mingqiang Wu","raw_affiliation_strings":["School of Computer and Electrical Engineering, Hunan University of Arts and Science, Changde, 415006, China"],"affiliations":[{"raw_affiliation_string":"School of Computer and Electrical Engineering, Hunan University of Arts and Science, Changde, 415006, China","institution_ids":["https://openalex.org/I196038209"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5003137580"],"corresponding_institution_ids":["https://openalex.org/I196038209"],"apc_list":{"value":1060,"currency":"GBP","value_usd":1300},"apc_paid":{"value":1060,"currency":"GBP","value_usd":1300},"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.14194512,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"12","issue":"1","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9998999834060669,"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":0.9998999834060669,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.9987999796867371,"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/T10028","display_name":"Topic Modeling","score":0.9962999820709229,"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/sentiment-analysis","display_name":"Sentiment analysis","score":0.5464000105857849},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5401999950408936},{"id":"https://openalex.org/keywords/categorical-variable","display_name":"Categorical variable","score":0.4984999895095825},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.498199999332428},{"id":"https://openalex.org/keywords/knowledge-graph","display_name":"Knowledge graph","score":0.4530999958515167},{"id":"https://openalex.org/keywords/identifier","display_name":"Identifier","score":0.4528000056743622},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4350000023841858},{"id":"https://openalex.org/keywords/multi-label-classification","display_name":"Multi-label classification","score":0.4023999869823456}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8342000246047974},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6509000062942505},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5900999903678894},{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.5464000105857849},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5401999950408936},{"id":"https://openalex.org/C5274069","wikidata":"https://www.wikidata.org/wiki/Q2285707","display_name":"Categorical variable","level":2,"score":0.4984999895095825},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.498199999332428},{"id":"https://openalex.org/C2987255567","wikidata":"https://www.wikidata.org/wiki/Q33002955","display_name":"Knowledge graph","level":2,"score":0.4530999958515167},{"id":"https://openalex.org/C154504017","wikidata":"https://www.wikidata.org/wiki/Q853614","display_name":"Identifier","level":2,"score":0.4528000056743622},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4350000023841858},{"id":"https://openalex.org/C2776482837","wikidata":"https://www.wikidata.org/wiki/Q3553958","display_name":"Multi-label classification","level":2,"score":0.4023999869823456},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.38830000162124634},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.3637999892234802},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.3312000036239624},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3280999958515167},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.3174000084400177},{"id":"https://openalex.org/C207685749","wikidata":"https://www.wikidata.org/wiki/Q2088941","display_name":"Domain knowledge","level":2,"score":0.31520000100135803},{"id":"https://openalex.org/C518677369","wikidata":"https://www.wikidata.org/wiki/Q202833","display_name":"Social media","level":2,"score":0.3005000054836273},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2824000120162964},{"id":"https://openalex.org/C197914299","wikidata":"https://www.wikidata.org/wiki/Q18650","display_name":"Semantic memory","level":3,"score":0.2816999852657318},{"id":"https://openalex.org/C71472368","wikidata":"https://www.wikidata.org/wiki/Q676880","display_name":"Text mining","level":2,"score":0.2800000011920929},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.2694999873638153},{"id":"https://openalex.org/C66945725","wikidata":"https://www.wikidata.org/wiki/Q18388823","display_name":"Text graph","level":3,"score":0.266400009393692},{"id":"https://openalex.org/C96711827","wikidata":"https://www.wikidata.org/wiki/Q17012245","display_name":"Entity linking","level":3,"score":0.26350000500679016}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1186/s40537-025-01276-6","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s40537-025-01276-6","pdf_url":"https://journalofbigdata.springeropen.com/counter/pdf/10.1186/s40537-025-01276-6","source":{"id":"https://openalex.org/S2737955091","display_name":"Journal Of Big Data","issn_l":"2196-1115","issn":["2196-1115"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Big Data","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:8248c394203d4b929d0ebc6bb6041ae5","is_oa":true,"landing_page_url":"https://doaj.org/article/8248c394203d4b929d0ebc6bb6041ae5","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Journal of Big Data, Vol 12, Iss 1, Pp 1-38 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1186/s40537-025-01276-6","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s40537-025-01276-6","pdf_url":"https://journalofbigdata.springeropen.com/counter/pdf/10.1186/s40537-025-01276-6","source":{"id":"https://openalex.org/S2737955091","display_name":"Journal Of Big Data","issn_l":"2196-1115","issn":["2196-1115"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Big Data","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320322191","display_name":"Hunan University","ror":"https://ror.org/05htk5m33"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4414597476.pdf","grobid_xml":"https://content.openalex.org/works/W4414597476.grobid-xml"},"referenced_works_count":66,"referenced_works":["https://openalex.org/W1832693441","https://openalex.org/W1888005072","https://openalex.org/W1984762104","https://openalex.org/W2087194317","https://openalex.org/W2149684865","https://openalex.org/W2154851992","https://openalex.org/W2163455955","https://openalex.org/W2251939518","https://openalex.org/W2902496224","https://openalex.org/W2911286998","https://openalex.org/W2962946486","https://openalex.org/W2984353870","https://openalex.org/W3034770463","https://openalex.org/W3037247064","https://openalex.org/W3095746859","https://openalex.org/W3104097132","https://openalex.org/W3114651754","https://openalex.org/W3154503084","https://openalex.org/W3159075545","https://openalex.org/W3188542058","https://openalex.org/W3204453541","https://openalex.org/W4207078300","https://openalex.org/W4210922939","https://openalex.org/W4221159394","https://openalex.org/W4235019172","https://openalex.org/W4280582438","https://openalex.org/W4287322212","https://openalex.org/W4289781031","https://openalex.org/W4307041079","https://openalex.org/W4308065851","https://openalex.org/W4312515269","https://openalex.org/W4316038090","https://openalex.org/W4318595446","https://openalex.org/W4321351836","https://openalex.org/W4324378562","https://openalex.org/W4381186951","https://openalex.org/W4383737134","https://openalex.org/W4385484551","https://openalex.org/W4385572552","https://openalex.org/W4388942639","https://openalex.org/W4389509840","https://openalex.org/W4389519277","https://openalex.org/W4391583905","https://openalex.org/W4391970302","https://openalex.org/W4392133405","https://openalex.org/W4392973748","https://openalex.org/W4394912432","https://openalex.org/W4395449067","https://openalex.org/W4400770878","https://openalex.org/W4402301037","https://openalex.org/W4402671904","https://openalex.org/W4403205326","https://openalex.org/W4404179154","https://openalex.org/W4405185121","https://openalex.org/W4406171687","https://openalex.org/W4406714256","https://openalex.org/W4407016464","https://openalex.org/W4407021633","https://openalex.org/W4407281215","https://openalex.org/W4409348166","https://openalex.org/W4409363984","https://openalex.org/W4410001189","https://openalex.org/W4410086879","https://openalex.org/W4410226599","https://openalex.org/W4410631979","https://openalex.org/W4411991746"],"related_works":[],"abstract_inverted_index":{"Abstract":[0],"Semi-supervised":[1],"classification":[2,30,46,69,238],"demonstrates":[3],"effective":[4],"performance":[5,217],"in":[6,75,218,237],"categorizing":[7],"short-length":[8],"texts,":[9,211],"such":[10],"as":[11,33,61,134],"social":[12],"media":[13],"posts":[14],"and":[15,56,90,97,130,152,171,202,205,240],"online":[16],"reviews,":[17],"through":[18,161],"the":[19,82,91,102,172,177,229],"utilization":[20],"of":[21,77,208],"limited":[22,203],"labeled":[23,52,204],"data.":[24],"Consequently,":[25],"semi-supervised":[26,39,119,215,235],"short":[27,40,120,140,153,191,210],"text":[28,41,121,192],"sentiment":[29,45,122,219],"has":[31],"emerged":[32],"a":[34,108,139],"significant":[35],"research":[36],"domain":[37],"within":[38,87],"classification.":[42,123],"However,":[43],"existing":[44],"methods":[47],"predominantly":[48],"rely":[49],"on":[50,223],"extensive":[51],"datasets":[53,226],"for":[54,68,118],"implementation":[55],"typically":[57],"treat":[58],"textual":[59,98],"labels":[60,88,96],"discrete":[62],"symbolic":[63],"representations":[64],"(e.g.,":[65],"categorical":[66],"identifiers":[67],"tasks).":[70],"This":[71],"conventional":[72],"method":[73,232],"results":[74],"oversight":[76],"two":[78],"critical":[79,185],"linguistic":[80,84],"dimensions:":[81],"inherent":[83],"characteristics":[85],"embedded":[86],"themselves":[89],"underlying":[92],"semantic":[93,132],"correlations":[94],"between":[95,149,199],"content.":[99],"To":[100],"address":[101],"limitations":[103],"above,":[104],"this":[105],"study":[106],"proposes":[107],"novel":[109],"Label":[110],"Knowledge-guided":[111],"Heterogeneous":[112],"Graph":[113],"Contrastive":[114],"Learning":[115],"(LKG-HGCL)":[116],"framework":[117,195],"Specifically,":[124],"we":[125],"incorporate":[126],"both":[127],"label-related":[128],"terms":[129],"their":[131],"expansions":[133],"label":[135,150,186,200],"knowledge":[136,142,201],"to":[137,183],"construct":[138],"text-label":[141],"heterogeneous":[143,157,168],"graph,":[144],"explicitly":[145],"modeling":[146],"dynamic":[147],"interactions":[148],"semantics":[151,187],"texts.":[154],"By":[155],"performing":[156],"graph":[158,169],"contrastive":[159,174],"learning":[160,175,216],"multi-relational":[162],"edge":[163],"augmentation,":[164,167],"adaptive":[165],"feature":[166],"encoding,":[170],"various":[173],"modes,":[176],"model":[178],"significantly":[179],"enhances":[180],"its":[181],"capability":[182],"capture":[184],"while":[188],"generating":[189],"optimized":[190],"embeddings.":[193],"The":[194],"establishes":[196],"robust":[197],"associations":[198],"large":[206],"amounts":[207],"unlabeled":[209],"thereby":[212],"effectively":[213],"improving":[214],"analysis.":[220],"Extensive":[221],"experiments":[222],"three":[224],"benchmark":[225],"demonstrate":[227],"that":[228],"proposed":[230],"LKG-HGCL":[231],"outperforms":[233],"state-of-the-art":[234],"approaches":[236],"accuracy":[239],"Macro-F1":[241],"metrics.":[242]},"counts_by_year":[],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}
