{"id":"https://openalex.org/W4206090639","doi":"https://doi.org/10.1109/ic-nidc54101.2021.9660505","title":"A Multi-feature Emotion Classification Model Based on LDA Subject Target Words","display_name":"A Multi-feature Emotion Classification Model Based on LDA Subject Target Words","publication_year":2021,"publication_date":"2021-11-17","ids":{"openalex":"https://openalex.org/W4206090639","doi":"https://doi.org/10.1109/ic-nidc54101.2021.9660505"},"language":"en","primary_location":{"id":"doi:10.1109/ic-nidc54101.2021.9660505","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ic-nidc54101.2021.9660505","pdf_url":null,"source":{"id":"https://openalex.org/S4363608589","display_name":"2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5010919859","display_name":"Shike Shao","orcid":null},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shike Shao","raw_affiliation_strings":["School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087613962","display_name":"Cui Ding","orcid":"https://orcid.org/0009-0009-8808-9436"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Cui Ding","raw_affiliation_strings":["School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100440326","display_name":"Lei Li","orcid":"https://orcid.org/0000-0002-5374-7293"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lei Li","raw_affiliation_strings":["School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I139759216"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.20479473,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"abs 1803 7771","issue":null,"first_page":"294","last_page":"298"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9955000281333923,"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":0.9926999807357788,"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":0.8038961291313171},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7003815174102783},{"id":"https://openalex.org/keywords/word-embedding","display_name":"Word embedding","score":0.6477816700935364},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6365578770637512},{"id":"https://openalex.org/keywords/sentence","display_name":"Sentence","score":0.6236365437507629},{"id":"https://openalex.org/keywords/variety","display_name":"Variety (cybernetics)","score":0.6201558113098145},{"id":"https://openalex.org/keywords/sentiment-analysis","display_name":"Sentiment analysis","score":0.6131229996681213},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.5780390501022339},{"id":"https://openalex.org/keywords/vocabulary","display_name":"Vocabulary","score":0.5771527886390686},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.4976332485675812},{"id":"https://openalex.org/keywords/subject","display_name":"Subject (documents)","score":0.46958842873573303},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.32915037870407104},{"id":"https://openalex.org/keywords/linguistics","display_name":"Linguistics","score":0.16688638925552368}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8038961291313171},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7003815174102783},{"id":"https://openalex.org/C2777462759","wikidata":"https://www.wikidata.org/wiki/Q18395344","display_name":"Word embedding","level":3,"score":0.6477816700935364},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6365578770637512},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.6236365437507629},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.6201558113098145},{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.6131229996681213},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5780390501022339},{"id":"https://openalex.org/C2777601683","wikidata":"https://www.wikidata.org/wiki/Q6499736","display_name":"Vocabulary","level":2,"score":0.5771527886390686},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.4976332485675812},{"id":"https://openalex.org/C2777855551","wikidata":"https://www.wikidata.org/wiki/Q12310021","display_name":"Subject (documents)","level":2,"score":0.46958842873573303},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.32915037870407104},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.16688638925552368},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C161191863","wikidata":"https://www.wikidata.org/wiki/Q199655","display_name":"Library science","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ic-nidc54101.2021.9660505","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ic-nidc54101.2021.9660505","pdf_url":null,"source":{"id":"https://openalex.org/S4363608589","display_name":"2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5199999809265137,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W1498436455","https://openalex.org/W1612003148","https://openalex.org/W2064675550","https://openalex.org/W2088622183","https://openalex.org/W2163605009","https://openalex.org/W2739774142","https://openalex.org/W2896457183","https://openalex.org/W2953297087","https://openalex.org/W2962824509","https://openalex.org/W2963240575"],"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/W2597655663","https://openalex.org/W3186997021","https://openalex.org/W4200618314","https://openalex.org/W4308088897","https://openalex.org/W4286432911"],"abstract_inverted_index":{"In":[0],"recent":[1],"years,":[2],"a":[3,56,73],"variety":[4],"of":[5,17,96,103,121,135,150,162],"sentiment":[6,94,101,108],"classification":[7,76,117],"models":[8,20],"based":[9,23,78],"on":[10,24,29,79,123],"deep":[11],"neural":[12],"networks":[13],"have":[14,45,59,67],"emerged.":[15],"Most":[16],"the":[18,52,85,92,99,106,115,128,141,148,158],"existing":[19],"are":[21],"trained":[22],"word":[25],"embedding,":[26],"or":[27,33],"rely":[28],"expensive":[30],"word-level":[31],"annotation,":[32],"use":[34],"sentence-level":[35],"annotation":[36],"only.":[37],"However,":[38],"some":[39],"important":[40],"linguistic":[41,53],"phenomena":[42],"and":[43,62,110],"resources":[44],"not":[46,138],"been":[47],"fully":[48],"studied.":[49],"Aiming":[50],"at":[51],"phenomenon":[54],"that":[55,127],"sentence":[57],"may":[58,66],"multiple":[60],"sentiments":[61,75,116,151],"different":[63,68],"target":[64,87,145],"words":[65,88],"sentiments,":[69],"this":[70],"thesis":[71],"proposes":[72],"multi-feature":[74,129],"model":[77,82,130],"LDA.":[80],"The":[81,133],"automatically":[83],"extracts":[84,98],"subject":[86],"through":[89],"LDA,":[90],"screens":[91],"global":[93],"features":[95,102,113],"sentences,":[97],"local":[100],"sentences":[104],"with":[105,114],"external":[107],"vocabulary,":[109],"integrates":[111],"various":[112],"model.":[118],"A":[119],"series":[120],"experiments":[122],"three":[124],"datasets":[125],"show":[126],"is":[131],"effective.":[132],"introduction":[134],"LDA":[136],"can":[137],"only":[139],"reduce":[140],"demand":[142],"for":[143],"labeled":[144],"words,":[146],"improve":[147],"accuracy":[149],"classification,":[152],"but":[153],"also":[154],"more":[155],"accurately":[156],"analyze":[157],"internal":[159],"emotional":[160],"trend":[161],"public":[163],"opinion":[164],"events.":[165]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}