{"id":"https://openalex.org/W4412871801","doi":"https://doi.org/10.1109/tem.2025.3589199","title":"Multimodal Sentiment Analysis of Online Product Marketing Information Based on Artificial Intelligence Neural Networks and Text Mining","display_name":"Multimodal Sentiment Analysis of Online Product Marketing Information Based on Artificial Intelligence Neural Networks and Text Mining","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4412871801","doi":"https://doi.org/10.1109/tem.2025.3589199"},"language":"en","primary_location":{"id":"doi:10.1109/tem.2025.3589199","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tem.2025.3589199","pdf_url":null,"source":{"id":"https://openalex.org/S154533451","display_name":"IEEE Transactions on Engineering Management","issn_l":"0018-9391","issn":["0018-9391","1558-0040"],"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 Engineering Management","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":null,"display_name":"Anzhong Huang","orcid":"https://orcid.org/0009-0000-6372-6181"},"institutions":[{"id":"https://openalex.org/I4210096899","display_name":"Jiangsu University of Science and Technology","ror":"https://ror.org/00tyjp878","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210096899"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Anzhong Huang","raw_affiliation_strings":["School of Economics and Management, Jiangsu University of Science and Technology, Zhenjiang, China"],"raw_orcid":"https://orcid.org/0009-0000-6372-6181","affiliations":[{"raw_affiliation_string":"School of Economics and Management, Jiangsu University of Science and Technology, Zhenjiang, China","institution_ids":["https://openalex.org/I4210096899"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033796472","display_name":"Fenfen Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I141103825","display_name":"Jiangxi University of Water Resources and Electric Power","ror":"https://ror.org/00avfj807","country_code":"CN","type":"education","lineage":["https://openalex.org/I141103825"]},{"id":"https://openalex.org/I4210120015","display_name":"Nanchang Institute of Science & Technology","ror":"https://ror.org/02q5y6156","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210120015"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fenfen Zhang","raw_affiliation_strings":["School of Finance and Economics, Nanchang Institute of Technology, Nanchang, China","School of finance and economics, Nanchang Institute of Technology, Nanchang, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Finance and Economics, Nanchang Institute of Technology, Nanchang, China","institution_ids":["https://openalex.org/I141103825"]},{"raw_affiliation_string":"School of finance and economics, Nanchang Institute of Technology, Nanchang, China","institution_ids":["https://openalex.org/I141103825","https://openalex.org/I4210120015"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5104228274","display_name":"Chunlai Song","orcid":"https://orcid.org/0009-0002-0997-9350"},"institutions":[{"id":"https://openalex.org/I4210113474","display_name":"Kyungil University","ror":"https://ror.org/024kwvm84","country_code":"KR","type":"education","lineage":["https://openalex.org/I4210113474"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Chunlai Song","raw_affiliation_strings":["Kyungil University, Gyeongsan, South Korea","Business Administration, Kyungil University, Gyeongsan, South Korea"],"raw_orcid":"https://orcid.org/0009-0002-0997-9350","affiliations":[{"raw_affiliation_string":"Kyungil University, Gyeongsan, South Korea","institution_ids":["https://openalex.org/I4210113474"]},{"raw_affiliation_string":"Business Administration, Kyungil University, Gyeongsan, South Korea","institution_ids":["https://openalex.org/I4210113474"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":9.772,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.97844621,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":"72","issue":null,"first_page":"3182","last_page":"3199"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9818000197410583,"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":0.9818000197410583,"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/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9495000243186951,"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/T12016","display_name":"Web Data Mining and Analysis","score":0.9330000281333923,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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":0.6693048477172852},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.65660560131073},{"id":"https://openalex.org/keywords/product","display_name":"Product (mathematics)","score":0.5199145674705505},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5099021196365356},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4840942621231079},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.3652607798576355},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09941193461418152}],"concepts":[{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.6693048477172852},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.65660560131073},{"id":"https://openalex.org/C90673727","wikidata":"https://www.wikidata.org/wiki/Q901718","display_name":"Product (mathematics)","level":2,"score":0.5199145674705505},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5099021196365356},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4840942621231079},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.3652607798576355},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09941193461418152},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tem.2025.3589199","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tem.2025.3589199","pdf_url":null,"source":{"id":"https://openalex.org/S154533451","display_name":"IEEE Transactions on Engineering Management","issn_l":"0018-9391","issn":["0018-9391","1558-0040"],"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 Engineering Management","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":56,"referenced_works":["https://openalex.org/W2619383789","https://openalex.org/W2767290858","https://openalex.org/W3037558128","https://openalex.org/W3096622396","https://openalex.org/W3098829134","https://openalex.org/W3107577028","https://openalex.org/W3116667534","https://openalex.org/W3132259035","https://openalex.org/W3153495133","https://openalex.org/W3155398915","https://openalex.org/W3170540448","https://openalex.org/W3175901146","https://openalex.org/W3183124605","https://openalex.org/W3185407146","https://openalex.org/W3197875592","https://openalex.org/W4200184869","https://openalex.org/W4210392637","https://openalex.org/W4210474990","https://openalex.org/W4213446791","https://openalex.org/W4214676882","https://openalex.org/W4220712276","https://openalex.org/W4225650823","https://openalex.org/W4225678079","https://openalex.org/W4283819695","https://openalex.org/W4304693606","https://openalex.org/W4309769112","https://openalex.org/W4312561705","https://openalex.org/W4313263102","https://openalex.org/W4319320626","https://openalex.org/W4362673398","https://openalex.org/W4376607634","https://openalex.org/W4381664826","https://openalex.org/W4382939671","https://openalex.org/W4384134026","https://openalex.org/W4385494537","https://openalex.org/W4385688016","https://openalex.org/W4385765961","https://openalex.org/W4387327976","https://openalex.org/W4387418633","https://openalex.org/W4388294554","https://openalex.org/W4388572082","https://openalex.org/W4388739381","https://openalex.org/W4389978636","https://openalex.org/W4390331593","https://openalex.org/W4390430684","https://openalex.org/W4390481322","https://openalex.org/W4391175994","https://openalex.org/W4391406986","https://openalex.org/W4391785380","https://openalex.org/W4392438912","https://openalex.org/W4398795445","https://openalex.org/W4399478675","https://openalex.org/W4399881971","https://openalex.org/W4399916721","https://openalex.org/W4406757382","https://openalex.org/W6796939779"],"related_works":["https://openalex.org/W2548633793","https://openalex.org/W3013279174","https://openalex.org/W2941935829","https://openalex.org/W2596247554","https://openalex.org/W3132372214","https://openalex.org/W4224284088","https://openalex.org/W4286571989","https://openalex.org/W2605642833","https://openalex.org/W2382028496","https://openalex.org/W3046268510"],"abstract_inverted_index":{"With":[0],"the":[1,86,131,154,159,174,190],"rise":[2],"of":[3,90,165,171,179,193],"multimodal":[4,60,95,194],"content":[5],"(such":[6],"as":[7],"text":[8,72],"and":[9,23,37,88,106,135,156,167,181,203,218],"images)":[10],"in":[11,46],"online":[12,215],"product":[13],"marketing,":[14],"sentiment":[15,44,61,91,195,210],"analysis":[16,62,196],"techniques":[17],"face":[18],"increasing":[19],"demands":[20],"for":[21,208],"accuracy":[22,164],"versatility.":[24],"However,":[25],"existing":[26,185],"approaches":[27],"often":[28],"struggle":[29],"with":[30,71,123],"modality":[31,82],"coordination,":[32],"deep":[33],"emotional":[34],"feature":[35,155],"extraction,":[36],"semantic":[38],"consistency,":[39],"which":[40],"hinder":[41],"effective":[42],"user":[43,209,220],"recognition":[45],"complex":[47],"marketing":[48,216],"contexts.":[49],"To":[50],"address":[51],"these":[52],"challenges,":[53],"this":[54],"work":[55,188],"proposes":[56],"a":[57,117,124,148,205],"hybrid":[58],"fusion":[59,150,199],"(HF-MSA)":[63],"model":[64,161],"that":[65],"combines":[66],"artificial":[67],"intelligence":[68],"neural":[69],"networks":[70,111],"mining":[73,96],"techniques.":[74],"The":[75],"focus":[76],"is":[77,98],"on":[78,173],"efficiently":[79],"integrating":[80],"heterogeneous":[81],"data":[83],"to":[84,112,141,200],"enhance":[85,219],"robustness":[87],"interpretability":[89],"analysis.":[92],"A":[93],"big-data-driven":[94],"framework":[97],"developed,":[99],"utilizing":[100],"bidirectional":[101,107],"encoder":[102],"representations":[103],"from":[104,197],"transformers":[105],"long":[108],"short-term":[109],"memory":[110],"extract":[113],"textual":[114],"features,":[115],"while":[116],"gated":[118],"graph":[119],"convolutional":[120],"network":[121],"combined":[122],"self-attention":[125],"mechanism":[126],"models":[127],"syntactic":[128],"dependencies.":[129],"For":[130],"image":[132],"modality,":[133],"channel":[134],"spatial":[136],"attention":[137],"modules":[138],"are":[139],"incorporated":[140],"improve":[142],"key":[143],"region":[144],"recognition.":[145],"Supported":[146],"by":[147],"dual-layer":[149],"strategy":[151],"at":[152],"both":[153],"decision":[157],"levels,":[158],"HF-MSA":[160],"achieves":[162],"an":[163,168],"77.43%":[166],"F1":[169],"score":[170],"72.84%":[172],"Twitter17":[175],"dataset,":[176],"reflecting":[177],"improvements":[178],"2.82%":[180],"3.02%,":[182],"respectively,":[183],"over":[184],"models.":[186],"This":[187],"advances":[189],"theoretical":[191],"development":[192],"static":[198],"dynamic":[201],"collaboration":[202],"provides":[204],"scalable":[206],"tool":[207],"insights,":[211],"helping":[212],"enterprises":[213],"optimize":[214],"strategies":[217],"engagement.":[221]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":3}],"updated_date":"2025-12-21T01:58:51.020947","created_date":"2025-10-10T00:00:00"}
