{"id":"https://openalex.org/W4415970958","doi":"https://doi.org/10.1109/tcss.2025.3619188","title":"A Scalable Multichannel Sentiment Analysis Model With Enhanced Semantic Understanding and Redundancy Reduction","display_name":"A Scalable Multichannel Sentiment Analysis Model With Enhanced Semantic Understanding and Redundancy Reduction","publication_year":2025,"publication_date":"2025-11-06","ids":{"openalex":"https://openalex.org/W4415970958","doi":"https://doi.org/10.1109/tcss.2025.3619188"},"language":null,"primary_location":{"id":"doi:10.1109/tcss.2025.3619188","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tcss.2025.3619188","pdf_url":null,"source":{"id":"https://openalex.org/S2490693980","display_name":"IEEE Transactions on Computational Social Systems","issn_l":"2329-924X","issn":["2329-924X","2373-7476"],"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 Computational Social Systems","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/A5100361668","display_name":"Jun Liu","orcid":"https://orcid.org/0000-0001-7390-8958"},"institutions":[{"id":"https://openalex.org/I10535382","display_name":"Chongqing University of Posts and Telecommunications","ror":"https://ror.org/03dgaqz26","country_code":"CN","type":"education","lineage":["https://openalex.org/I10535382"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jun Liu","raw_affiliation_strings":["School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China"],"raw_orcid":"https://orcid.org/0000-0001-7390-8958","affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China","institution_ids":["https://openalex.org/I10535382"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Xiang Li","orcid":"https://orcid.org/0009-0004-0564-9527"},"institutions":[{"id":"https://openalex.org/I10535382","display_name":"Chongqing University of Posts and Telecommunications","ror":"https://ror.org/03dgaqz26","country_code":"CN","type":"education","lineage":["https://openalex.org/I10535382"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiang Li","raw_affiliation_strings":["School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China"],"raw_orcid":"https://orcid.org/0009-0004-0564-9527","affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China","institution_ids":["https://openalex.org/I10535382"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101851003","display_name":"Ming\u2010Wei Lin","orcid":"https://orcid.org/0000-0003-2026-7178"},"institutions":[{"id":"https://openalex.org/I111753288","display_name":"Fujian Normal University","ror":"https://ror.org/020azk594","country_code":"CN","type":"education","lineage":["https://openalex.org/I111753288"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mingwei Lin","raw_affiliation_strings":["College of Computer and Cyber Security, Fujian Normal University, Fuzhou, China"],"raw_orcid":"https://orcid.org/0000-0003-2026-7178","affiliations":[{"raw_affiliation_string":"College of Computer and Cyber Security, Fujian Normal University, Fuzhou, China","institution_ids":["https://openalex.org/I111753288"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5088955392","display_name":"Xin Luo","orcid":"https://orcid.org/0000-0002-1348-5305"},"institutions":[{"id":"https://openalex.org/I142108993","display_name":"Southwest University","ror":"https://ror.org/01kj4z117","country_code":"CN","type":"education","lineage":["https://openalex.org/I142108993"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xin Luo","raw_affiliation_strings":["College of Computer and Information Science, Southwest University, Chongqing, China"],"raw_orcid":"https://orcid.org/0000-0002-1348-5305","affiliations":[{"raw_affiliation_string":"College of Computer and Information Science, Southwest University, Chongqing, China","institution_ids":["https://openalex.org/I142108993"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.792,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.91999686,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":98},"biblio":{"volume":"13","issue":"2","first_page":"1513","last_page":"1528"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9567999839782715,"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":0.9567999839782715,"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/T10028","display_name":"Topic Modeling","score":0.006399999838322401,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.00279999990016222,"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":0.7918000221252441},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.6288999915122986},{"id":"https://openalex.org/keywords/redundancy","display_name":"Redundancy (engineering)","score":0.585099995136261},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5175999999046326},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.4223000109195709},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.41119998693466187},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.390500009059906},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.3716999888420105},{"id":"https://openalex.org/keywords/semantic-similarity","display_name":"Semantic similarity","score":0.3700000047683716}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.886900007724762},{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.7918000221252441},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6531000137329102},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.6288999915122986},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.585099995136261},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5175999999046326},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.4223000109195709},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4187999963760376},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.41119998693466187},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.390500009059906},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.3716999888420105},{"id":"https://openalex.org/C130318100","wikidata":"https://www.wikidata.org/wiki/Q2268914","display_name":"Semantic similarity","level":2,"score":0.3700000047683716},{"id":"https://openalex.org/C2781122975","wikidata":"https://www.wikidata.org/wiki/Q16928266","display_name":"Semantic feature","level":2,"score":0.36800000071525574},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35120001435279846},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.34940001368522644},{"id":"https://openalex.org/C117978034","wikidata":"https://www.wikidata.org/wiki/Q5422192","display_name":"Extractor","level":2,"score":0.3452000021934509},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.3424000144004822},{"id":"https://openalex.org/C2778493491","wikidata":"https://www.wikidata.org/wiki/Q7449072","display_name":"Semantic matching","level":3,"score":0.31150001287460327},{"id":"https://openalex.org/C511149849","wikidata":"https://www.wikidata.org/wiki/Q7449051","display_name":"Semantic computing","level":3,"score":0.3059000074863434},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3052999973297119},{"id":"https://openalex.org/C86034646","wikidata":"https://www.wikidata.org/wiki/Q474311","display_name":"Semantic gap","level":4,"score":0.30160000920295715},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.296099990606308},{"id":"https://openalex.org/C197914299","wikidata":"https://www.wikidata.org/wiki/Q18650","display_name":"Semantic memory","level":3,"score":0.2921999990940094},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.28850001096725464},{"id":"https://openalex.org/C112933361","wikidata":"https://www.wikidata.org/wiki/Q2845258","display_name":"Probabilistic latent semantic analysis","level":2,"score":0.2874000072479248},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2833000123500824},{"id":"https://openalex.org/C85407183","wikidata":"https://www.wikidata.org/wiki/Q1045785","display_name":"Semantic network","level":2,"score":0.27549999952316284},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.26159998774528503},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2599000036716461},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.25189998745918274}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tcss.2025.3619188","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tcss.2025.3619188","pdf_url":null,"source":{"id":"https://openalex.org/S2490693980","display_name":"IEEE Transactions on Computational Social Systems","issn_l":"2329-924X","issn":["2329-924X","2373-7476"],"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 Computational Social Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1501038647","display_name":null,"funder_award_id":"62307008","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6563435177","display_name":null,"funder_award_id":"72171066","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8723057676","display_name":null,"funder_award_id":"CSTB2025NSCQ-GPX1309","funder_id":"https://openalex.org/F4320327865","funder_display_name":"Chongqing Research Program of Basic Research and Frontier Technology"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320327865","display_name":"Chongqing Research Program of Basic Research and Frontier Technology","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0,84,209],"prevailing":[1],"techniques":[2],"in":[3,44,180,200,248],"textual":[4],"sentiment":[5,24,52,64,77,82,87,210,226],"analysis":[6,25,65],"include":[7],"convolutional":[8],"neural":[9],"networks":[10],"(CNNs),":[11],"bidirectional":[12],"long":[13],"short-term":[14],"memory":[15],"(BiLSTM)":[16],"networks,":[17],"and":[18,80,106,267,274,291,296],"attention":[19,134,164],"mechanisms.":[20],"However,":[21],"the":[22,49,113,140,149,178,181,190,222,231,258,270,285,294,302],"existing":[23],"methods":[26],"typically":[27],"focus":[28],"on":[29,195,218,269,293],"relationships":[30],"among":[31],"neighboring":[32,105],"words,":[33],"neglecting":[34],"long-range":[35,127,153],"dependencies.":[36],"Moreover,":[37],"they":[38],"fail":[39],"to":[40,97,116,125,142,155,169,172,176,220,233,243,301],"reduce":[41,177],"redundant":[42],"information":[43],"semantic":[45,101,118,145,157,182,206],"features,":[46,119,198],"ultimately":[47],"affecting":[48],"effectiveness":[50],"of":[51,68,151,204,225],"analysis.":[53],"To":[54,110],"tackle":[55],"these":[56],"challenges,":[57],"this":[58],"article":[59],"introduces":[60],"a":[61,74,81,91,120,132,161,201],"novel":[62],"text":[63],"model":[66,191,232,260,286],"consisting":[67],"three":[69],"key":[70,196],"modules:":[71],"word":[72,108],"embedding,":[73],"scalable":[75,85,92],"multichannel":[76,86,173],"feature":[78,88],"extractor,":[79],"classifier.":[83],"extractor":[89],"employs":[90],"N-gram":[93],"dilated":[94],"CNN":[95],"(SN-DCNN)":[96],"extract":[98],"multiscale":[99,144],"high-level":[100],"by":[102,185,263,289],"capturing":[103],"both":[104],"nonneighboring":[107],"associations.":[109],"further":[111],"enhance":[112],"model\u2019s":[114],"ability":[115],"capture":[117,126,143],"BiLSTM":[121,141],"network":[122],"is":[123,137,167],"introduced":[124,168],"dependencies":[128,154],"within":[129],"sentences.":[130],"Subsequently,":[131],"structured":[133],"(SA)":[135],"mechanism":[136,165],"incorporated":[138],"after":[139],"information,":[146],"thereby":[147],"enabling":[148],"mapping":[150],"individual":[152],"distinct":[156],"levels.":[158],"In":[159],"addition,":[160],"semantics-driven":[162],"channel":[163],"(SDCAM)":[166],"assign":[170],"weights":[171],"structures,":[174],"aiming":[175],"redundancy":[179],"features.":[183],"Furthermore,":[184],"integrating":[186],"multihead":[187],"self-attention":[188],"(MHSA),":[189],"can":[192],"effectively":[193],"concentrate":[194],"sentiment-related":[197],"resulting":[199],"comprehensive":[202],"enhancement":[203],"its":[205,236],"representation":[207],"capability.":[208],"classifier":[211],"utilizes":[212],"an":[213],"adaptive":[214],"loss":[215],"function":[216,229],"based":[217],"cross-entropy":[219],"correct":[221],"probability":[223],"distribution":[224],"classes.":[227],"This":[228],"enables":[230],"dynamically":[234],"adjust":[235],"learning":[237],"capacity":[238],"for":[239],"each":[240],"class,":[241],"leading":[242],"significant":[244],"performance":[245],"improvements,":[246],"especially":[247],"datasets":[249],"with":[250],"imbalanced":[251],"class":[252],"distributions.":[253],"Experimental":[254],"results":[255],"show":[256],"that":[257],"proposed":[259],"improves":[261,287],"accuracy":[262,288],"0.94%,":[264],"0.96%,":[265],"0.36%,":[266],"0.86%":[268],"NLPCC2017-ECGC,":[271],"ChnSentiCorp,":[272],"COLD,":[273],"IMDB":[275],"datasets,":[276,298],"respectively,":[277,299],"when":[278],"using":[279],"word-level":[280],"embeddings.":[281],"With":[282],"character-level":[283],"embeddings,":[284],"0.76%":[290],"1.23%":[292],"ChnSentiCorp":[295],"COLD":[297],"compared":[300],"state-of-the-art":[303],"methods.":[304]},"counts_by_year":[{"year":2026,"cited_by_count":2}],"updated_date":"2026-04-03T16:38:21.277918","created_date":"2025-11-06T00:00:00"}
