{"id":"https://openalex.org/W7152446506","doi":"https://doi.org/10.1145/3774904.3792577","title":"ATGFB-MFF: Adaptive Text-Guided Fiber Bundle Feature Fusion with LLMs for Multimodal Sentiment Analysis and Emotion Recognition in Conversations","display_name":"ATGFB-MFF: Adaptive Text-Guided Fiber Bundle Feature Fusion with LLMs for Multimodal Sentiment Analysis and Emotion Recognition in Conversations","publication_year":2026,"publication_date":"2026-04-09","ids":{"openalex":"https://openalex.org/W7152446506","doi":"https://doi.org/10.1145/3774904.3792577"},"language":null,"primary_location":{"id":"doi:10.1145/3774904.3792577","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3774904.3792577","pdf_url":null,"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 ACM Web Conference 2026","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3774904.3792577","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5018103970","display_name":"Zhaowei Liu","orcid":"https://orcid.org/0000-0003-3262-8962"},"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"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zhaowei Liu","raw_affiliation_strings":["Yantai University, Yantai, Shandong, China"],"raw_orcid":"https://orcid.org/0000-0003-3262-8962","affiliations":[{"raw_affiliation_string":"Yantai University, Yantai, Shandong, China","institution_ids":["https://openalex.org/I18452120"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Sheng Liu","orcid":"https://orcid.org/0009-0005-7160-6602"},"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"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Sheng Liu","raw_affiliation_strings":["Yantai University, Yantai, Shandong, China"],"raw_orcid":"https://orcid.org/0009-0005-7160-6602","affiliations":[{"raw_affiliation_string":"Yantai University, Yantai, Shandong, China","institution_ids":["https://openalex.org/I18452120"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076122416","display_name":"Weiqing Yan","orcid":"https://orcid.org/0000-0001-7869-2404"},"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"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weiqing Yan","raw_affiliation_strings":["Yantai University, Yantai, Shandong, China"],"raw_orcid":"https://orcid.org/0000-0001-7869-2404","affiliations":[{"raw_affiliation_string":"Yantai University, Yantai, Shandong, China","institution_ids":["https://openalex.org/I18452120"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009279384","display_name":"Peng Song","orcid":"https://orcid.org/0000-0002-6567-663X"},"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"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Peng Song","raw_affiliation_strings":["Yantai University, Yantai, Shandong, China"],"raw_orcid":"https://orcid.org/0000-0002-6567-663X","affiliations":[{"raw_affiliation_string":"Yantai University, Yantai, Shandong, China","institution_ids":["https://openalex.org/I18452120"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070423020","display_name":"Yongchao Song","orcid":"https://orcid.org/0000-0002-5737-368X"},"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"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yongchao Song","raw_affiliation_strings":["Yantai University, Yantai, Shandong, China"],"raw_orcid":"https://orcid.org/0000-0002-5737-368X","affiliations":[{"raw_affiliation_string":"Yantai University, Yantai, Shandong, China","institution_ids":["https://openalex.org/I18452120"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5133247528","display_name":"Rufei Gao","orcid":"https://orcid.org/0009-0005-8508-5297"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rufei Gao","raw_affiliation_strings":["Yantai Institute of Technology, Yantai, Shandong, China"],"raw_orcid":"https://orcid.org/0009-0005-8508-5297","affiliations":[{"raw_affiliation_string":"Yantai Institute of Technology, Yantai, Shandong, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5018103970"],"corresponding_institution_ids":["https://openalex.org/I18452120"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.79424241,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"7442","last_page":"7453"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10667","display_name":"Emotion and Mood Recognition","score":0.6664000153541565,"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":0.6664000153541565,"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":0.22669999301433563,"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/T12031","display_name":"Speech and dialogue systems","score":0.010599999688565731,"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/feature","display_name":"Feature (linguistics)","score":0.5910000205039978},{"id":"https://openalex.org/keywords/sentiment-analysis","display_name":"Sentiment analysis","score":0.5558000206947327},{"id":"https://openalex.org/keywords/emotion-recognition","display_name":"Emotion recognition","score":0.4009000062942505},{"id":"https://openalex.org/keywords/fusion","display_name":"Fusion","score":0.3772999942302704},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.35339999198913574},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.2971000075340271}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6173999905586243},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5910000205039978},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5853999853134155},{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.5558000206947327},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4523000121116638},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.41449999809265137},{"id":"https://openalex.org/C2777438025","wikidata":"https://www.wikidata.org/wiki/Q1339090","display_name":"Emotion recognition","level":2,"score":0.4009000062942505},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.3772999942302704},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.35339999198913574},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2971000075340271},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.2702000141143799},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.2651999890804291},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2619999945163727},{"id":"https://openalex.org/C2778134712","wikidata":"https://www.wikidata.org/wiki/Q1047307","display_name":"Bundle","level":2,"score":0.2574000060558319}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3774904.3792577","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3774904.3792577","pdf_url":null,"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 ACM Web Conference 2026","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3774904.3792577","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3774904.3792577","pdf_url":null,"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 ACM Web Conference 2026","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":44,"referenced_works":["https://openalex.org/W2883409523","https://openalex.org/W2963341956","https://openalex.org/W2963686995","https://openalex.org/W2964010806","https://openalex.org/W2964051877","https://openalex.org/W2964346351","https://openalex.org/W3034266838","https://openalex.org/W3093051361","https://openalex.org/W3173396651","https://openalex.org/W3214432797","https://openalex.org/W4221154966","https://openalex.org/W4225303565","https://openalex.org/W4281701185","https://openalex.org/W4283372217","https://openalex.org/W4285294723","https://openalex.org/W4285802067","https://openalex.org/W4304091726","https://openalex.org/W4308222497","https://openalex.org/W4360930863","https://openalex.org/W4361195282","https://openalex.org/W4380355289","https://openalex.org/W4382246105","https://openalex.org/W4384918448","https://openalex.org/W4385570923","https://openalex.org/W4385571170","https://openalex.org/W4385573848","https://openalex.org/W4386043468","https://openalex.org/W4387969507","https://openalex.org/W4389523777","https://openalex.org/W4391828195","https://openalex.org/W4393146934","https://openalex.org/W4396819361","https://openalex.org/W4401042765","https://openalex.org/W4402429398","https://openalex.org/W4403791023","https://openalex.org/W4405032761","https://openalex.org/W4406903235","https://openalex.org/W4407569461","https://openalex.org/W4409190866","https://openalex.org/W4409349992","https://openalex.org/W4409362748","https://openalex.org/W4409728756","https://openalex.org/W4411446656","https://openalex.org/W4411892026"],"related_works":[],"abstract_inverted_index":{"Multimodal":[0],"Sentiment":[1],"Analysis":[2],"(MSA)":[3],"and":[4,27,62,98,106,136],"Emotion":[5],"Recognition":[6],"in":[7,16,57,82,170],"Conversations":[8],"(ERC)":[9],"have":[10],"rapidly":[11],"developed":[12],"into":[13,91,114],"pivotal":[14],"tasks":[15],"artificial":[17],"intelligence.":[18],"Large":[19],"Language":[20],"Models":[21],"(LLMs)":[22],"offer":[23],"powerful":[24],"semantic":[25,96],"reasoning":[26],"computational":[28],"capabilities,":[29],"showing":[30],"great":[31],"potential":[32,173],"for":[33,118,176],"understanding":[34],"emotional":[35],"content.":[36],"However,":[37],"when":[38],"applied":[39],"to":[40,51,102,144],"multimodal":[41,74,89,177],"sentiment":[42,75,178],"data,":[43,55],"LLMs":[44,175],"face":[45],"significant":[46],"challenges,":[47,69],"including":[48],"the":[49,109,164,172],"inability":[50],"directly":[52],"process":[53],"heterogeneous":[54],"difficulties":[56],"coping":[58],"with":[59],"feature":[60],"misalignment":[61],"suboptimal":[63],"cross-modal":[64],"fusion.":[65,107],"To":[66],"address":[67],"these":[68],"we":[70],"propose":[71],"a":[72],"novel":[73],"inference":[76,120],"framework":[77],"named":[78],"ATGFB-MFF":[79,156],"which":[80,141],"grounded":[81],"fiber":[83,99,137],"bundle":[84],"theory.":[85],"This":[86],"method":[87],"decomposes":[88],"features":[90,111],"an":[92],"adaptive":[93],"text-guided":[94],"shared":[95,132],"space":[97,133],"offset":[100,138],"spaces":[101],"achieve":[103],"structured":[104,115],"alignment":[105],"Then":[108],"fused":[110],"are":[112,142],"converted":[113],"pseudo-token":[116],"sequences":[117],"effective":[119],"via":[121],"frozen":[122],"LLMs.":[123],"We":[124],"also":[125],"introduce":[126],"two":[127],"loss":[128,135,140],"functions":[129],"respectively":[130],"called":[131],"consistency":[134],"regularization":[139],"used":[143],"improve":[145],"representation":[146],"stability.":[147],"Extensive":[148],"experiments":[149],"on":[150],"four":[151],"benchmark":[152],"datasets":[153],"demonstrate":[154],"that":[155],"consistently":[157],"outperforms":[158],"state-of-the-art":[159],"baselines.":[160],"These":[161],"results":[162],"highlight":[163],"efficacy":[165],"of":[166,174],"geometric":[167],"structural":[168],"modeling":[169],"unlocking":[171],"inference.":[179]},"counts_by_year":[],"updated_date":"2026-04-29T09:16:38.111599","created_date":"2026-04-10T00:00:00"}