{"id":"https://openalex.org/W7136474456","doi":"https://doi.org/10.1109/tetci.2026.3671067","title":"DualKanbaFormer: An Efficient Selective Sparse Framework for Multimodal Aspect-Based Sentiment Analysis","display_name":"DualKanbaFormer: An Efficient Selective Sparse Framework for Multimodal Aspect-Based Sentiment Analysis","publication_year":2026,"publication_date":"2026-03-16","ids":{"openalex":"https://openalex.org/W7136474456","doi":"https://doi.org/10.1109/tetci.2026.3671067"},"language":null,"primary_location":{"id":"doi:10.1109/tetci.2026.3671067","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tetci.2026.3671067","pdf_url":null,"source":{"id":"https://openalex.org/S4210210251","display_name":"IEEE Transactions on Emerging Topics in Computational Intelligence","issn_l":"2471-285X","issn":["2471-285X"],"is_oa":false,"is_in_doaj":false,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Emerging Topics in Computational Intelligence","raw_type":"journal-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/A5040557587","display_name":"Adamu Lawan","orcid":"https://orcid.org/0000-0002-7245-1703"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Adamu Lawan","raw_affiliation_strings":["Beihang University"],"raw_orcid":"https://orcid.org/0009-0009-3857-8792","affiliations":[{"raw_affiliation_string":"Beihang University","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101069493","display_name":"Juhua Pu","orcid":"https://orcid.org/0000-0003-3866-8703"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Juhua Pu","raw_affiliation_strings":["Beihang University"],"raw_orcid":"https://orcid.org/0000-0003-3866-8703","affiliations":[{"raw_affiliation_string":"Beihang University","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129640263","display_name":"Haruna Yunusa","orcid":null},"institutions":[{"id":"https://openalex.org/I4210146971","display_name":"MetraLabs (Germany)","ror":"https://ror.org/04zqv4n59","country_code":"DE","type":"company","lineage":["https://openalex.org/I4210146971"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Haruna Yunusa","raw_affiliation_strings":["NewraLab"],"raw_orcid":"https://orcid.org/0000-0001-6736-2135","affiliations":[{"raw_affiliation_string":"NewraLab","institution_ids":["https://openalex.org/I4210146971"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111325639","display_name":"Aliyu Umar","orcid":null},"institutions":[{"id":"https://openalex.org/I63072094","display_name":"University of Portsmouth","ror":"https://ror.org/03ykbk197","country_code":"GB","type":"education","lineage":["https://openalex.org/I63072094"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Aliyu Umar","raw_affiliation_strings":["University of Portsmouth"],"raw_orcid":"https://orcid.org/0000-0002-2058-7828","affiliations":[{"raw_affiliation_string":"University of Portsmouth","institution_ids":["https://openalex.org/I63072094"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129580394","display_name":"Muhammad Lawan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Muhammad Lawan","raw_affiliation_strings":["Federal University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Federal University","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027499584","display_name":"Adamu Sani Yahaya","orcid":"https://orcid.org/0000-0003-4183-7221"},"institutions":[{"id":"https://openalex.org/I919958821","display_name":"Bayero University Kano","ror":"https://ror.org/049pzty39","country_code":"NG","type":"education","lineage":["https://openalex.org/I919958821"]}],"countries":["NG"],"is_corresponding":false,"raw_author_name":"Adamu Sani Yahaya","raw_affiliation_strings":["Bayero University"],"raw_orcid":"https://orcid.org/0000-0003-4183-7221","affiliations":[{"raw_affiliation_string":"Bayero University","institution_ids":["https://openalex.org/I919958821"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5099292795","display_name":"Mahmoud Basi","orcid":null},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mahmoud Basi","raw_affiliation_strings":["Beihang University"],"raw_orcid":"https://orcid.org/0009-0009-5731-2818","affiliations":[{"raw_affiliation_string":"Beihang University","institution_ids":["https://openalex.org/I82880672"]}]}],"institutions":[],"countries_distinct_count":4,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":7.8105,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.96067133,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":"10","issue":"4","first_page":"2843","last_page":"2855"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.8324000239372253,"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":0.8324000239372253,"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/T10667","display_name":"Emotion and Mood Recognition","score":0.11829999834299088,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.0027000000700354576,"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":0.5127000212669373},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.26269999146461487},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.25690001249313354},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.25459998846054077}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6664999723434448},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5626999735832214},{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.5127000212669373},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.35589998960494995},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3093999922275543},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.288100004196167},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.26269999146461487},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.25690001249313354},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.25459998846054077},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.23070000112056732}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tetci.2026.3671067","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tetci.2026.3671067","pdf_url":null,"source":{"id":"https://openalex.org/S4210210251","display_name":"IEEE Transactions on Emerging Topics in Computational Intelligence","issn_l":"2471-285X","issn":["2471-285X"],"is_oa":false,"is_in_doaj":false,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Emerging Topics in Computational Intelligence","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3753167796","display_name":null,"funder_award_id":"62577006","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"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W2194775991","https://openalex.org/W2562607067","https://openalex.org/W2891778157","https://openalex.org/W2964051877","https://openalex.org/W2964164368","https://openalex.org/W2998470965","https://openalex.org/W3094502228","https://openalex.org/W3187172219","https://openalex.org/W4214875575","https://openalex.org/W4293205624","https://openalex.org/W4389987717","https://openalex.org/W4390618491","https://openalex.org/W4391057690","https://openalex.org/W4392385305","https://openalex.org/W4393346089","https://openalex.org/W4399995409","https://openalex.org/W4402053001","https://openalex.org/W4402979613","https://openalex.org/W4404780702","https://openalex.org/W4404792820","https://openalex.org/W4405302041","https://openalex.org/W4406856822","https://openalex.org/W4408353322","https://openalex.org/W4409917222","https://openalex.org/W4410087476","https://openalex.org/W4411962479","https://openalex.org/W4413145332"],"related_works":[],"abstract_inverted_index":{"Multimodal":[0],"Aspect-Based":[1],"Sentiment":[2],"Analysis":[3],"(MABSA)":[4],"enhances":[5],"sentiment":[6],"detection":[7],"by":[8,143],"integrating":[9],"text":[10],"with":[11,39,51,80,100],"complementary":[12],"modalities,":[13],"such":[14],"as":[15],"images,":[16],"for":[17,88],"a":[18,77,170],"more":[19],"comprehensive":[20],"understanding":[21],"of":[22,60,68,111],"aspect-level":[23],"sentiment.":[24],"Despite":[25],"notable":[26],"progress,":[27],"existing":[28],"MABSA":[29],"methods":[30],"still":[31],"lack":[32],"strong":[33],"aspect":[34],"awareness,":[35],"particularly":[36],"in":[37],"sentences":[38],"multiple":[40],"aspects":[41,128],"and":[42,62,83,114,129,153,159,166,185],"conflicting":[43],"sentiments":[44],"or":[45],"when":[46],"visual":[47,186],"cues":[48],"are":[49],"misaligned":[50],"the":[52,66,93,109,120,134],"text.":[53],"Together,":[54],"these":[55,72],"issues":[56],"hinder":[57],"accurate":[58],"modeling":[59,144],"aspect-focused":[61],"cross-modal":[63,178],"dependencies,":[64],"limiting":[65],"effectiveness":[67],"MABSA.":[69,89],"To":[70,149],"address":[71],"challenges,":[73],"we":[74],"propose":[75],"DualKanbaFormer,":[76],"novel":[78],"framework":[79],"parallel":[81],"Textual":[82],"Visual":[84],"KanbaFormer":[85],"modules":[86],"tailored":[87],"At":[90],"its":[91],"core,":[92],"model":[94,138],"introduces":[95],"Aspect-Driven":[96],"Sparse":[97],"Attention":[98],"(ADSA)":[99],"an":[101],"aspect-aware":[102],"attention":[103,116],"gating":[104],"mechanism,":[105],"which":[106],"dynamically":[107],"regulates":[108,177],"contributions":[110],"Scope,":[112],"Focus,":[113],"Proximity":[115],"branches":[117],"conditioned":[118],"on":[119,189],"target":[121],"aspect.":[122],"This":[123],"ensures":[124],"precise":[125],"alignment":[126,142,182],"between":[127,183],"opinion":[130],"signals.":[131],"In":[132],"addition,":[133],"selective":[135],"state":[136],"space":[137],"Mamba":[139],"strengthens":[140],"aspect\u2013opinion":[141],"long-range":[145],"dependencies":[146],"across":[147],"modalities.":[148],"enhance":[150],"representational":[151],"expressiveness":[152],"training":[154],"stability,":[155],"Kolmogorov\u2013Arnold":[156],"Networks":[157],"(KANs)":[158],"Dynamic":[160],"Tanh":[161],"(DyT)":[162],"replace":[163],"conventional":[164],"feed-forward":[165],"normalization":[167],"layers.":[168],"Finally,":[169],"CNN-based":[171],"multimodal":[172],"gated":[173],"fusion":[174],"module":[175],"adaptively":[176],"interactions,":[179],"ensuring":[180],"robust":[181],"textual":[184],"features.":[187],"Experiments":[188],"two":[190],"benchmark":[191],"datasets":[192],"demonstrate":[193],"that":[194],"DualKanbaFormer":[195],"outperforms":[196],"several":[197],"state-of-the-art":[198],"models.":[199]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-08-12T21:12:35.861297","created_date":"2026-03-17T00:00:00"}
