{"id":"https://openalex.org/W4284970925","doi":"https://doi.org/10.1111/exsy.13096","title":"Effective deep learning based multimodal sentiment analysis from unstructured big data","display_name":"Effective deep learning based multimodal sentiment analysis from unstructured big data","publication_year":2022,"publication_date":"2022-07-09","ids":{"openalex":"https://openalex.org/W4284970925","doi":"https://doi.org/10.1111/exsy.13096"},"language":"en","primary_location":{"id":"doi:10.1111/exsy.13096","doi":"https://doi.org/10.1111/exsy.13096","is_oa":false,"landing_page_url":"https://doi.org/10.1111/exsy.13096","pdf_url":null,"source":{"id":"https://openalex.org/S72232612","display_name":"Expert Systems","issn_l":"0266-4720","issn":["0266-4720","1468-0394"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-scientific","ccf-c","cwts-core","dongbi-c","fnege-4","jufo-1","norway-1","poland-70","russia-white-list-1"],"host_organization":"https://openalex.org/P4310320595","host_organization_name":"Wiley","host_organization_lineage":["https://openalex.org/P4310320595"],"host_organization_lineage_names":["Wiley"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Expert Systems","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"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/A5019478558","display_name":"Swasthika Jain T J","orcid":"https://orcid.org/0000-0002-5943-3973","observed_orcids":["https://orcid.org/0000-0002-5943-3973"]},"institutions":[{"id":"https://openalex.org/I885392262","display_name":"GITAM University","ror":"https://ror.org/0440p1d37","country_code":"IN","type":"education","lineage":["https://openalex.org/I885392262"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Swasthika Jain Thandaga Jwalanaiah","raw_affiliation_strings":["Department of Computer Science and Engineering, GITAM School of Technology, Bangalore, India"],"raw_orcid":"https://orcid.org/0000-0002-5943-3973","affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, GITAM School of Technology, Bangalore, India","institution_ids":["https://openalex.org/I885392262"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071685762","display_name":"Israel Jeena Jacob","orcid":"https://orcid.org/0000-0001-6706-1017","observed_orcids":["https://orcid.org/0000-0001-6706-1017"]},"institutions":[{"id":"https://openalex.org/I885392262","display_name":"GITAM University","ror":"https://ror.org/0440p1d37","country_code":"IN","type":"education","lineage":["https://openalex.org/I885392262"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Israel Jeena Jacob","raw_affiliation_strings":["CSE, GITAM School of Technology, Bangalore, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"CSE, GITAM School of Technology, Bangalore, India","institution_ids":["https://openalex.org/I885392262"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5018681609","display_name":"Ajay Kumar Mandava","orcid":"https://orcid.org/0009-0001-8902-6914","observed_orcids":["https://orcid.org/0009-0001-8902-6914"]},"institutions":[{"id":"https://openalex.org/I885392262","display_name":"GITAM University","ror":"https://ror.org/0440p1d37","country_code":"IN","type":"education","lineage":["https://openalex.org/I885392262"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Ajay Kumar Mandava","raw_affiliation_strings":["EECE, GITAM School of Technology, Bangalore, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"EECE, GITAM School of Technology, Bangalore, India","institution_ids":["https://openalex.org/I885392262"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5019478558"],"corresponding_institution_ids":["https://openalex.org/I885392262"],"apc_list":{"value":3750,"currency":"USD","value_usd":3750},"apc_paid":null,"fwci":1.0585,"has_fulltext":false,"cited_by_count":19,"citation_normalized_percentile":{"value":0.76209127,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":98},"biblio":{"volume":"40","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":0.9980000257492065,"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":0.9980000257492065,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.0013000000035390258,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T12262","display_name":"Hate Speech and Cyberbullying Detection","score":9.999999747378752e-05,"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/multimodal-sentiment-analysis","display_name":"multimodal sentiment analysis","score":0.9990000128746033},{"id":"https://openalex.org/keywords/social-media-posts","display_name":"social media posts","score":0.6830000281333923},{"id":"https://openalex.org/keywords/visual-language","display_name":"visual language","score":0.5950000286102295},{"id":"https://openalex.org/keywords/stacked-recurrent-neural-networks","display_name":"stacked recurrent neural networks","score":0.7509999871253967},{"id":"https://openalex.org/keywords/deep-convolutional-neural-networks","display_name":"deep convolutional neural networks","score":0.8040000200271606},{"id":"https://openalex.org/keywords/self-attention","display_name":"self-attention","score":0.8519999980926514},{"id":"https://openalex.org/keywords/fine-grained-sentiment-classification","display_name":"fine-grained sentiment classification","score":0.8180000185966492},{"id":"https://openalex.org/keywords/big-data","display_name":"big data","score":0.37400001287460327}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8313770294189453},{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.6798582077026367},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6004800796508789},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5624356269836426},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5213807225227356},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.5160977244377136},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.4915388524532318},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.43381911516189575},{"id":"https://openalex.org/C519991488","wikidata":"https://www.wikidata.org/wiki/Q28865","display_name":"Python (programming language)","level":2,"score":0.4141334593296051},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3895268142223358},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.356812983751297},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.33922839164733887},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3291560411453247},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1111/exsy.13096","doi":"https://doi.org/10.1111/exsy.13096","is_oa":false,"landing_page_url":"https://doi.org/10.1111/exsy.13096","pdf_url":null,"source":{"id":"https://openalex.org/S72232612","display_name":"Expert Systems","issn_l":"0266-4720","issn":["0266-4720","1468-0394"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-scientific","ccf-c","cwts-core","dongbi-c","fnege-4","jufo-1","norway-1","poland-70","russia-white-list-1"],"host_organization":"https://openalex.org/P4310320595","host_organization_name":"Wiley","host_organization_lineage":["https://openalex.org/P4310320595"],"host_organization_lineage_names":["Wiley"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Expert Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":48,"referenced_works":["https://openalex.org/W2062913298","https://openalex.org/W2084046180","https://openalex.org/W2096707493","https://openalex.org/W2112193258","https://openalex.org/W2115023510","https://openalex.org/W2302914195","https://openalex.org/W2762383441","https://openalex.org/W2798802604","https://openalex.org/W2803017359","https://openalex.org/W2910191085","https://openalex.org/W2922183696","https://openalex.org/W2943547402","https://openalex.org/W2951478627","https://openalex.org/W2963702064","https://openalex.org/W2965096309","https://openalex.org/W2969078176","https://openalex.org/W2970215282","https://openalex.org/W2982216702","https://openalex.org/W2986660805","https://openalex.org/W2999678654","https://openalex.org/W3003801295","https://openalex.org/W3007102898","https://openalex.org/W3015527453","https://openalex.org/W3032532364","https://openalex.org/W3039503982","https://openalex.org/W3040450405","https://openalex.org/W3041730883","https://openalex.org/W3045162186","https://openalex.org/W3046166186","https://openalex.org/W3092606432","https://openalex.org/W3093043896","https://openalex.org/W3094290105","https://openalex.org/W3094310321","https://openalex.org/W3095024162","https://openalex.org/W3096393000","https://openalex.org/W3102100346","https://openalex.org/W3103167052","https://openalex.org/W3109940244","https://openalex.org/W3111911126","https://openalex.org/W3112718522","https://openalex.org/W3131504710","https://openalex.org/W3154098986","https://openalex.org/W3158436118","https://openalex.org/W3159597990","https://openalex.org/W3176189029","https://openalex.org/W3178621454","https://openalex.org/W3213870678","https://openalex.org/W4302028121"],"related_works":["https://openalex.org/W4415836904","https://openalex.org/W2910191085","https://openalex.org/W3046561365","https://openalex.org/W4386350795","https://openalex.org/W3041730883","https://openalex.org/W2969078176","https://openalex.org/W2922183696","https://openalex.org/W4386088239","https://openalex.org/W2982281530","https://openalex.org/W4413432314"],"abstract_inverted_index":{"Abstract":[0],"More":[1],"recently,":[2],"as":[3,19,87,144],"images,":[4,88],"memes":[5],"and":[6,30,65,90,109,131,147,153,170,191,219,230,246,254,256,272,277],"graphics":[7],"interchange":[8],"formats":[9],"have":[10],"dominated":[11],"social":[12,22],"feeds,":[13],"typographic/infographic":[14],"visual":[15,35,64,203,255,276],"content":[16,67],"has":[17,43],"emerged":[18],"an":[20,214],"important":[21],"media":[23],"component.":[24],"This":[25,71],"multimodal":[26,78,84,91,257],"text":[27,29,89,92,98,124,154,158,253],"combines":[28],"image,":[31],"defining":[32],"a":[33,74,162,210],"novel":[34],"language":[36],"that":[37,262],"must":[38],"be":[39],"analysed":[40,143],"because":[41],"it":[42],"the":[44,51,63,101,105,110,123,126,129,150,176,179,199,206,221,238,242,263],"potential":[45],"to":[46,58,174,197,202,217,281],"modify,":[47],"confirm":[48],"or":[49],"grade":[50],"sentiment's":[52],"polarity.":[53],"The":[54,97,119,157,232],"problem":[55],"is":[56,195,235],"how":[57],"effectively":[59],"use":[60],"information":[61],"from":[62,125],"textual":[66],"in":[68,116,237],"image\u2010text":[69],"posts.":[70],"article":[72],"presents":[73],"new":[75],"deep":[76,182],"learning\u2010based":[77],"sentiment":[79,177,226,250],"analysis":[80,251],"(MSA)":[81],"model":[82],"using":[83,128,241],"data":[85],"such":[86],"(image":[93],"with":[94,167,188],"embedded":[95],"text).":[96],"analytic":[99,107],"unit,":[100,104],"discretization":[102,120],"control":[103],"picture":[106,127],"component":[108,112,208],"decision\u2010making":[111],"are":[113,141],"all":[114],"included":[115],"this":[117],"system.":[118],"unit":[121],"separates":[122],"variant":[130],"channel":[132],"augmented":[133],"maximally":[134],"stable":[135],"extremal":[136],"regions":[137],"(VCA\u2010MSERs)":[138],"technique,":[139],"which":[140],"then":[142],"discrete":[145],"elements":[146],"fed":[148],"into":[149,223],"appropriate":[151],"image":[152],"analytics":[155,159],"units.":[156],"system":[160],"utilizes":[161],"stacked":[163],"recurrent":[164],"neural":[165,184],"network":[166,185],"multilevel":[168],"attention":[169],"feedback":[171],"module":[172,193],"(SRNN\u2010MAFM)":[173],"detect":[175],"of":[178,252,269],"text.":[180,258],"A":[181],"convolutional":[183],"(CNN)":[186],"structure":[187],"parallel\u2010dilated":[189],"convolution":[190],"self\u2010attention":[192],"(PDC\u2010SAM)":[194],"developed":[196],"forecast":[198],"emotional":[200],"response":[201],"content.":[204],"Finally,":[205],"decision":[207],"employs":[209],"Boolean":[211],"framework":[212],"including":[213],"OR":[215],"function":[216],"evaluate":[218],"classify":[220],"output":[222],"three":[224],"fine\u2010grained":[225],"classes:":[227],"positive,":[228],"neutral":[229],"negative.":[231],"proposed":[233],"work":[234],"simulated":[236],"python":[239],"platform":[240],"STS\u2010Gold,":[243],"Flickr":[244],"8k":[245],"B\u2010T4SA":[247],"datasets":[248],"for":[249,274],"Simulation":[259],"outcomes":[260],"proved":[261],"suggested":[264],"method":[265],"achieved":[266],"better":[267],"accuracy":[268],"97.8%,":[270],"97.7%":[271],"90%":[273],"text,":[275],"MSA":[278],"individually":[279],"compared":[280],"other":[282],"methods.":[283]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":5}],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2022-07-10T00:00:00"}