{"id":"https://openalex.org/W4372341197","doi":"https://doi.org/10.1109/icassp49357.2023.10097030","title":"MMATR: A Lightweight Approach for Multimodal Sentiment Analysis Based on Tensor Methods","display_name":"MMATR: A Lightweight Approach for Multimodal Sentiment Analysis Based on Tensor Methods","publication_year":2023,"publication_date":"2023-05-05","ids":{"openalex":"https://openalex.org/W4372341197","doi":"https://doi.org/10.1109/icassp49357.2023.10097030"},"language":"en","primary_location":{"id":"doi:10.1109/icassp49357.2023.10097030","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp49357.2023.10097030","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-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/A5051381280","display_name":"Panagiotis Koromilas","orcid":"https://orcid.org/0000-0002-4466-463X"},"institutions":[{"id":"https://openalex.org/I200777214","display_name":"National and Kapodistrian University of Athens","ror":"https://ror.org/04gnjpq42","country_code":"GR","type":"education","lineage":["https://openalex.org/I200777214"]}],"countries":["GR"],"is_corresponding":true,"raw_author_name":"Panagiotis Koromilas","raw_affiliation_strings":["National and Kapodistrian University of Athens,Department of Informatics and Telecommunications","Department of Informatics and Telecommunications, National and Kapodistrian University of Athens"],"affiliations":[{"raw_affiliation_string":"National and Kapodistrian University of Athens,Department of Informatics and Telecommunications","institution_ids":["https://openalex.org/I200777214"]},{"raw_affiliation_string":"Department of Informatics and Telecommunications, National and Kapodistrian University of Athens","institution_ids":["https://openalex.org/I200777214"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017437981","display_name":"Mihalis A. Nicolaou","orcid":"https://orcid.org/0000-0001-9175-477X"},"institutions":[{"id":"https://openalex.org/I148518341","display_name":"Cyprus Institute","ror":"https://ror.org/01q8k8p90","country_code":"CY","type":"other","lineage":["https://openalex.org/I148518341"]}],"countries":["CY"],"is_corresponding":false,"raw_author_name":"Mihalis A. Nicolaou","raw_affiliation_strings":["The Cyprus Institute"],"affiliations":[{"raw_affiliation_string":"The Cyprus Institute","institution_ids":["https://openalex.org/I148518341"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069190127","display_name":"\u0398\u03b5\u03cc\u03b4\u03c9\u03c1\u03bf\u03c2 \u0393\u03b9\u03b1\u03bd\u03bd\u03b1\u03ba\u03cc\u03c0\u03bf\u03c5\u03bb\u03bf\u03c2","orcid":"https://orcid.org/0000-0003-1634-824X"},"institutions":[{"id":"https://openalex.org/I203474044","display_name":"National Centre of Scientific Research \"Demokritos\"","ror":"https://ror.org/038jp4m40","country_code":"GR","type":"facility","lineage":["https://openalex.org/I203474044"]}],"countries":["GR"],"is_corresponding":false,"raw_author_name":"Theodoros Giannakopoulos","raw_affiliation_strings":["NCSR Demokritos"],"affiliations":[{"raw_affiliation_string":"NCSR Demokritos","institution_ids":["https://openalex.org/I203474044"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5050734738","display_name":"Yannis Panagakis","orcid":"https://orcid.org/0000-0003-0153-5210"},"institutions":[{"id":"https://openalex.org/I200777214","display_name":"National and Kapodistrian University of Athens","ror":"https://ror.org/04gnjpq42","country_code":"GR","type":"education","lineage":["https://openalex.org/I200777214"]}],"countries":["GR"],"is_corresponding":false,"raw_author_name":"Yannis Panagakis","raw_affiliation_strings":["National and Kapodistrian University of Athens,Department of Informatics and Telecommunications","Department of Informatics and Telecommunications, National and Kapodistrian University of Athens"],"affiliations":[{"raw_affiliation_string":"National and Kapodistrian University of Athens,Department of Informatics and Telecommunications","institution_ids":["https://openalex.org/I200777214"]},{"raw_affiliation_string":"Department of Informatics and Telecommunications, National and Kapodistrian University of Athens","institution_ids":["https://openalex.org/I200777214"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5051381280"],"corresponding_institution_ids":["https://openalex.org/I200777214"],"apc_list":null,"apc_paid":null,"fwci":1.4939,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.80285036,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12303","display_name":"Tensor decomposition and applications","score":0.9980000257492065,"subfield":{"id":"https://openalex.org/subfields/2605","display_name":"Computational Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12303","display_name":"Tensor decomposition and applications","score":0.9980000257492065,"subfield":{"id":"https://openalex.org/subfields/2605","display_name":"Computational Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9847999811172485,"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":0.9815999865531921,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7747485637664795},{"id":"https://openalex.org/keywords/tensor","display_name":"Tensor (intrinsic definition)","score":0.643306314945221},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5921014547348022},{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.5381999611854553},{"id":"https://openalex.org/keywords/parameterized-complexity","display_name":"Parameterized complexity","score":0.5111988186836243},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4728584289550781},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4677201807498932},{"id":"https://openalex.org/keywords/flattening","display_name":"Flattening","score":0.4501594305038452},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.41970789432525635},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.32185226678848267},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.2695292830467224},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.15577024221420288}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7747485637664795},{"id":"https://openalex.org/C155281189","wikidata":"https://www.wikidata.org/wiki/Q3518150","display_name":"Tensor (intrinsic definition)","level":2,"score":0.643306314945221},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5921014547348022},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.5381999611854553},{"id":"https://openalex.org/C165464430","wikidata":"https://www.wikidata.org/wiki/Q1570441","display_name":"Parameterized complexity","level":2,"score":0.5111988186836243},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4728584289550781},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4677201807498932},{"id":"https://openalex.org/C19444555","wikidata":"https://www.wikidata.org/wiki/Q212750","display_name":"Flattening","level":2,"score":0.4501594305038452},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.41970789432525635},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.32185226678848267},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2695292830467224},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15577024221420288},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp49357.2023.10097030","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp49357.2023.10097030","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320327859","display_name":"Hellenic Foundation for Research and Innovation","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W2544822491","https://openalex.org/W2738900641","https://openalex.org/W2787581402","https://openalex.org/W2803686446","https://openalex.org/W2883409523","https://openalex.org/W2899197626","https://openalex.org/W2946218857","https://openalex.org/W2962931510","https://openalex.org/W2963523721","https://openalex.org/W2964010806","https://openalex.org/W2964051877","https://openalex.org/W2964216663","https://openalex.org/W2964346351","https://openalex.org/W3034266838","https://openalex.org/W3034849760","https://openalex.org/W3037572520","https://openalex.org/W3087647883","https://openalex.org/W3093051361","https://openalex.org/W3098019619","https://openalex.org/W3128412859","https://openalex.org/W3141688548","https://openalex.org/W3157910549","https://openalex.org/W6729203000","https://openalex.org/W6741969688","https://openalex.org/W6752724743","https://openalex.org/W6766298470","https://openalex.org/W6797781246"],"related_works":["https://openalex.org/W2049450268","https://openalex.org/W3163663844","https://openalex.org/W2076772710","https://openalex.org/W2046237991","https://openalex.org/W2051058708","https://openalex.org/W2627522194","https://openalex.org/W2504233681","https://openalex.org/W2946123365","https://openalex.org/W2015179278","https://openalex.org/W2390326991"],"abstract_inverted_index":{"Despite":[0],"the":[1,13,22,47,51,92,95,104,119,125,129,157,165,168],"considerable":[2],"research":[3],"output":[4],"on":[5],"Multimodal":[6,52,171],"Learning":[7],"for":[8,34,77],"Affect-related":[9],"tasks,":[10],"most":[11],"of":[12,21,24,72,94,121,128,145,151,170,178],"current":[14],"methods":[15],"are":[16,115],"very":[17],"complex":[18],"in":[19,46,167],"terms":[20],"number":[23,120],"trainable":[25],"parameters,":[26,122],"and":[27,98,110,132],"thus":[28],"do":[29],"not":[30],"constitute":[31],"effective":[32],"solutions":[33],"real-life":[35],"applications.":[36],"In":[37],"this":[38,44],"work":[39],"we":[40,154],"try":[41],"to":[42,82,117,164],"alleviate":[43],"gap":[45],"literature":[48],"by":[49,87,107],"introducing":[50],"Attention":[53],"Tensor":[54],"Regression":[55],"(MMATR)":[56],"network,":[57],"a":[58,66,89,134,143,149],"lightweight":[59],"model":[60],"that":[61,114,138,156],"is":[62],"based":[63],"on:":[64],"(i)":[65],"static":[67],"input":[68],"representation":[69],"(2D":[70],"matrix":[71],"dimensions":[73],"time":[74],"\u00d7":[75],"features)":[76],"each":[78],"modality,":[79],"which":[80],"helps":[81],"avoid":[83],"high-parameterized":[84],"sequential":[85],"models":[86],"incorporating":[88],"CNN,":[90],"(ii)":[91],"replacement":[93],"usual":[96],"pooling":[97],"flattening":[99],"operations":[100],"as":[101,103],"well":[102],"linear":[105],"layers":[106,113],"tensor":[108,111],"contraction":[109],"regression":[112],"able":[116],"reduce":[118],"while":[123],"keeping":[124],"high-order":[126],"structure":[127],"multimodal":[130,140],"data,":[131],"(iii)":[133],"bimodal":[135],"attention":[136],"layer":[137],"learns":[139],"co-occurrences.":[141],"By":[142],"set":[144],"experiments":[146],"comparing":[147],"with":[148],"variety":[150],"state-of-the-art":[152,166],"techniques,":[153],"show":[155],"proposed":[158],"MMATR":[159],"can":[160],"achieve":[161],"results":[162],"competitive":[163],"task":[169],"Sentiment":[172],"Analysis,":[173],"albeit":[174],"having":[175],"four":[176],"orders":[177],"magnitude":[179],"fewer":[180],"parameters.":[181]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":1}],"updated_date":"2026-03-27T14:29:43.386196","created_date":"2025-10-10T00:00:00"}