{"id":"https://openalex.org/W4386222771","doi":"https://doi.org/10.1142/s0218126624500865","title":"Research on Text Emotion Analysis Based on BMCBMA Model in Online Education Environment","display_name":"Research on Text Emotion Analysis Based on BMCBMA Model in Online Education Environment","publication_year":2023,"publication_date":"2023-08-28","ids":{"openalex":"https://openalex.org/W4386222771","doi":"https://doi.org/10.1142/s0218126624500865"},"language":"en","primary_location":{"id":"doi:10.1142/s0218126624500865","is_oa":false,"landing_page_url":"https://doi.org/10.1142/s0218126624500865","pdf_url":null,"source":{"id":"https://openalex.org/S167602672","display_name":"Journal of Circuits Systems and Computers","issn_l":"0218-1266","issn":["0218-1266","1793-6454"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319815","host_organization_name":"World Scientific","host_organization_lineage":["https://openalex.org/P4310319815"],"host_organization_lineage_names":["World Scientific"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Circuits, Systems and Computers","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/A5101564469","display_name":"M. Tian","orcid":"https://orcid.org/0009-0009-9002-5933"},"institutions":[{"id":"https://openalex.org/I2802584641","display_name":"Leshan Normal University","ror":"https://ror.org/036cvz290","country_code":"CN","type":"education","lineage":["https://openalex.org/I2802584641"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Mijuan Tian","raw_affiliation_strings":["School of Educational Sciences, Leshan Normal University, Leshan City, Sichuan Province, 614000, P. R. China"],"raw_orcid":"https://orcid.org/0009-0009-9002-5933","affiliations":[{"raw_affiliation_string":"School of Educational Sciences, Leshan Normal University, Leshan City, Sichuan Province, 614000, P. R. China","institution_ids":["https://openalex.org/I2802584641"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5075871120","display_name":"Rong Fu","orcid":"https://orcid.org/0000-0002-8235-4130"},"institutions":[{"id":"https://openalex.org/I2802584641","display_name":"Leshan Normal University","ror":"https://ror.org/036cvz290","country_code":"CN","type":"education","lineage":["https://openalex.org/I2802584641"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Rong Fu","raw_affiliation_strings":["School of Educational Sciences, Leshan Normal University, Leshan City, Sichuan Province, 614000, P. R. China"],"raw_orcid":"https://orcid.org/0000-0002-8235-4130","affiliations":[{"raw_affiliation_string":"School of Educational Sciences, Leshan Normal University, Leshan City, Sichuan Province, 614000, P. R. China","institution_ids":["https://openalex.org/I2802584641"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5101564469"],"corresponding_institution_ids":["https://openalex.org/I2802584641"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.10416248,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"33","issue":"05","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":0.9937000274658203,"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":0.9937000274658203,"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/T11122","display_name":"Online Learning and Analytics","score":0.9869999885559082,"subfield":{"id":"https://openalex.org/subfields/1706","display_name":"Computer Science Applications"},"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":0.9559000134468079,"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/computer-science","display_name":"Computer science","score":0.7849820256233215},{"id":"https://openalex.org/keywords/polysemy","display_name":"Polysemy","score":0.7027996182441711},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6429265737533569},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.632705569267273},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.5454121828079224},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.5264254808425903},{"id":"https://openalex.org/keywords/word-embedding","display_name":"Word embedding","score":0.47293710708618164},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.4624727964401245},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.4580897092819214},{"id":"https://openalex.org/keywords/recall","display_name":"Recall","score":0.41385650634765625},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.41278302669525146},{"id":"https://openalex.org/keywords/linguistics","display_name":"Linguistics","score":0.12461096048355103},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.12355640530586243},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.12343773245811462},{"id":"https://openalex.org/keywords/cognitive-psychology","display_name":"Cognitive psychology","score":0.11925724148750305}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7849820256233215},{"id":"https://openalex.org/C2780276568","wikidata":"https://www.wikidata.org/wiki/Q191928","display_name":"Polysemy","level":2,"score":0.7027996182441711},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6429265737533569},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.632705569267273},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.5454121828079224},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.5264254808425903},{"id":"https://openalex.org/C2777462759","wikidata":"https://www.wikidata.org/wiki/Q18395344","display_name":"Word embedding","level":3,"score":0.47293710708618164},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.4624727964401245},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.4580897092819214},{"id":"https://openalex.org/C100660578","wikidata":"https://www.wikidata.org/wiki/Q18733","display_name":"Recall","level":2,"score":0.41385650634765625},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.41278302669525146},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.12461096048355103},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.12355640530586243},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.12343773245811462},{"id":"https://openalex.org/C180747234","wikidata":"https://www.wikidata.org/wiki/Q23373","display_name":"Cognitive psychology","level":1,"score":0.11925724148750305},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1142/s0218126624500865","is_oa":false,"landing_page_url":"https://doi.org/10.1142/s0218126624500865","pdf_url":null,"source":{"id":"https://openalex.org/S167602672","display_name":"Journal of Circuits Systems and Computers","issn_l":"0218-1266","issn":["0218-1266","1793-6454"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319815","host_organization_name":"World Scientific","host_organization_lineage":["https://openalex.org/P4310319815"],"host_organization_lineage_names":["World Scientific"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Circuits, Systems and Computers","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.8500000238418579}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W2101537501","https://openalex.org/W2105577927","https://openalex.org/W2606042658","https://openalex.org/W2800742438","https://openalex.org/W2941799245","https://openalex.org/W2958619243","https://openalex.org/W2963477629","https://openalex.org/W2986485696","https://openalex.org/W3000065748","https://openalex.org/W3000739907","https://openalex.org/W3015657179","https://openalex.org/W3022935508","https://openalex.org/W3102257629","https://openalex.org/W3107577028","https://openalex.org/W3134320197","https://openalex.org/W3146366485","https://openalex.org/W3196409540","https://openalex.org/W4200277175","https://openalex.org/W4210843599","https://openalex.org/W4221071532","https://openalex.org/W4303422666","https://openalex.org/W4313398738","https://openalex.org/W4367281558","https://openalex.org/W4392233009"],"related_works":["https://openalex.org/W2376040010","https://openalex.org/W2613880225","https://openalex.org/W2788559978","https://openalex.org/W2358036664","https://openalex.org/W2891304714","https://openalex.org/W4385239993","https://openalex.org/W2310152915","https://openalex.org/W2362895247","https://openalex.org/W4285531126","https://openalex.org/W2353607782"],"abstract_inverted_index":{"This":[0],"paper":[1],"suggests":[2],"a":[3,34,49,163],"text":[4,26,43,124,197],"emotion":[5,44,198],"analysis":[6,45,199],"approach":[7,192],"built":[8],"on":[9],"the":[10,22,73,78,82,89,92,95,118,147,168,173,201],"BMCBMA":[11],"model":[12,51,101,134],"for":[13,122],"use":[14],"in":[15,29,41,77,125,196],"an":[16],"online":[17],"education":[18],"environment":[19],"aiming":[20],"at":[21],"issues":[23],"of":[24,91,144,156,183],"poor":[25],"utilization,":[27],"challenge":[28],"effective":[30],"information":[31,107,121,143],"extraction,":[32],"and":[33,81,113,160,180,187],"failure":[35],"to":[36,71,104,116,138,152,158,161],"effectively":[37],"recognize":[38],"word":[39,63,74],"polysemy":[40],"existing":[42],"task":[46],"research.":[47],"Initially,":[48],"language":[50],"is":[52,69,85,102,135,150],"constructed":[53],"using":[54,88],"Bidirectional":[55],"Encoder":[56],"Representations":[57],"from":[58],"Transformers":[59],"(BERT)":[60],"pre-training.":[61],"The":[62,128,190],"vector":[64,75,84],"trained":[65,76],"by":[66,108],"BERT":[67],"pre-training":[68],"selected":[70],"supplant":[72],"conventional":[79],"manner,":[80],"semantic":[83,106,165],"generated":[86],"dynamically":[87],"context":[90],"word.":[93],"Second,":[94],"Bi-directional":[96],"Long":[97],"Short-Term":[98],"Memory":[99],"(BiLSTM)":[100],"employed":[103],"record":[105],"simultaneously":[109],"integrating":[110],"both":[111],"positive":[112],"negative":[114],"orientations":[115],"assess":[117],"emotional":[119],"polarity":[120],"every":[123],"its":[126],"entirety.":[127],"Multi-channel":[129],"Convolutional":[130],"Neural":[131],"Networks":[132],"(CNN)":[133],"then":[136],"utilized":[137],"derive":[139,162],"textual":[140],"local":[141],"feature":[142],"text.":[145],"Lastly,":[146],"attention":[148],"mechanism":[149],"used":[151],"cause":[153],"all":[154],"types":[155],"features":[157],"interact":[159],"deeper":[164],"association":[166],"within":[167],"context.":[169],"Experiments":[170],"indicate":[171],"that":[172],"suggested":[174,191],"method":[175],"has":[176],"accuracy,":[177],"F1,":[178],"recall,":[179],"precision":[181],"values":[182],"0.915,":[184],"0.911,":[185],"0.915":[186],"0.908,":[188],"correspondingly.":[189],"performs":[193],"significantly":[194],"better":[195],"than":[200],"comparison":[202],"method.":[203]},"counts_by_year":[],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}
