{"id":"https://openalex.org/W4392254190","doi":"https://doi.org/10.1145/3639479.3639490","title":"A Multi-dimension and Multi-granularity Feature Fusion Method for Chinese Microblog Sentiment Classification","display_name":"A Multi-dimension and Multi-granularity Feature Fusion Method for Chinese Microblog Sentiment Classification","publication_year":2023,"publication_date":"2023-12-27","ids":{"openalex":"https://openalex.org/W4392254190","doi":"https://doi.org/10.1145/3639479.3639490"},"language":"en","primary_location":{"id":"doi:10.1145/3639479.3639490","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3639479.3639490","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 6th International Conference on Machine Learning and Natural Language Processing","raw_type":"proceedings-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/A5087233311","display_name":"Tingting Wei","orcid":"https://orcid.org/0000-0003-0531-8598"},"institutions":[{"id":"https://openalex.org/I101479585","display_name":"South China Agricultural University","ror":"https://ror.org/05v9jqt67","country_code":"CN","type":"education","lineage":["https://openalex.org/I101479585"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Tingting Wei","raw_affiliation_strings":["College of Mathematics and Informatics, South China Agricultural University, China"],"affiliations":[{"raw_affiliation_string":"College of Mathematics and Informatics, South China Agricultural University, China","institution_ids":["https://openalex.org/I101479585"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Xiaoyue Liu","orcid":"https://orcid.org/0009-0001-2569-7460"},"institutions":[{"id":"https://openalex.org/I101479585","display_name":"South China Agricultural University","ror":"https://ror.org/05v9jqt67","country_code":"CN","type":"education","lineage":["https://openalex.org/I101479585"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoyue Liu","raw_affiliation_strings":["College of Mathematics and Informatics, South China Agricultural University, China"],"affiliations":[{"raw_affiliation_string":"College of Mathematics and Informatics, South China Agricultural University, China","institution_ids":["https://openalex.org/I101479585"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102757262","display_name":"Leyao Qu","orcid":"https://orcid.org/0009-0007-8359-237X"},"institutions":[{"id":"https://openalex.org/I101479585","display_name":"South China Agricultural University","ror":"https://ror.org/05v9jqt67","country_code":"CN","type":"education","lineage":["https://openalex.org/I101479585"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Leyao Qu","raw_affiliation_strings":["College of Mathematics and Informatics, South China Agricultural University, China"],"affiliations":[{"raw_affiliation_string":"College of Mathematics and Informatics, South China Agricultural University, China","institution_ids":["https://openalex.org/I101479585"]}]},{"author_position":"last","author":{"id":null,"display_name":"Yanxia Chen","orcid":"https://orcid.org/0009-0000-8706-6413"},"institutions":[{"id":"https://openalex.org/I101479585","display_name":"South China Agricultural University","ror":"https://ror.org/05v9jqt67","country_code":"CN","type":"education","lineage":["https://openalex.org/I101479585"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yanxia Chen","raw_affiliation_strings":["College of Mathematics and Informatics, South China Agricultural University, China"],"affiliations":[{"raw_affiliation_string":"College of Mathematics and Informatics, South China Agricultural University, China","institution_ids":["https://openalex.org/I101479585"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5087233311"],"corresponding_institution_ids":["https://openalex.org/I101479585"],"apc_list":null,"apc_paid":null,"fwci":0.1718,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.60871793,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"56","last_page":"61"},"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":0.9983999729156494,"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":0.9973999857902527,"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":0.8556106090545654},{"id":"https://openalex.org/keywords/word2vec","display_name":"Word2vec","score":0.732608437538147},{"id":"https://openalex.org/keywords/microblogging","display_name":"Microblogging","score":0.6731387972831726},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6727281808853149},{"id":"https://openalex.org/keywords/sentiment-analysis","display_name":"Sentiment analysis","score":0.6446855068206787},{"id":"https://openalex.org/keywords/social-media","display_name":"Social media","score":0.5885732173919678},{"id":"https://openalex.org/keywords/word-embedding","display_name":"Word embedding","score":0.578898549079895},{"id":"https://openalex.org/keywords/granularity","display_name":"Granularity","score":0.532707154750824},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.47928255796432495},{"id":"https://openalex.org/keywords/sentence","display_name":"Sentence","score":0.4669163227081299},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.43110933899879456},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.4259083569049835},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.4177696704864502},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3334195017814636},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.22095811367034912},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.11103770136833191}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8556106090545654},{"id":"https://openalex.org/C2776461190","wikidata":"https://www.wikidata.org/wiki/Q22673982","display_name":"Word2vec","level":3,"score":0.732608437538147},{"id":"https://openalex.org/C143275388","wikidata":"https://www.wikidata.org/wiki/Q92438","display_name":"Microblogging","level":3,"score":0.6731387972831726},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6727281808853149},{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.6446855068206787},{"id":"https://openalex.org/C518677369","wikidata":"https://www.wikidata.org/wiki/Q202833","display_name":"Social media","level":2,"score":0.5885732173919678},{"id":"https://openalex.org/C2777462759","wikidata":"https://www.wikidata.org/wiki/Q18395344","display_name":"Word embedding","level":3,"score":0.578898549079895},{"id":"https://openalex.org/C177774035","wikidata":"https://www.wikidata.org/wiki/Q1246948","display_name":"Granularity","level":2,"score":0.532707154750824},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.47928255796432495},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.4669163227081299},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.43110933899879456},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.4259083569049835},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.4177696704864502},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3334195017814636},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.22095811367034912},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.11103770136833191},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","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/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3639479.3639490","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3639479.3639490","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 6th International Conference on Machine Learning and Natural Language Processing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Quality Education","score":0.7599999904632568,"id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W2768663569","https://openalex.org/W2790250716","https://openalex.org/W2993668409","https://openalex.org/W3008003435","https://openalex.org/W3081031588","https://openalex.org/W3102911373","https://openalex.org/W3109813099","https://openalex.org/W3156232408","https://openalex.org/W3193322990","https://openalex.org/W3212038008","https://openalex.org/W4205807230","https://openalex.org/W6600719526","https://openalex.org/W6603999813"],"related_works":["https://openalex.org/W2370508628","https://openalex.org/W1540611520","https://openalex.org/W3003606604","https://openalex.org/W2946409105","https://openalex.org/W2795129682","https://openalex.org/W3040974839","https://openalex.org/W2985392712","https://openalex.org/W4388996947","https://openalex.org/W3133567596","https://openalex.org/W2798009317"],"abstract_inverted_index":{"Chinese":[0,62,194],"microblog":[1,63,195],"comments":[2],"exhibit":[3,42],"a":[4,58],"wide":[5],"range":[6],"of":[7,30,120,178,193],"expressions,":[8],"including":[9],"brief":[10],"terms":[11],"and":[12,24,35,72,75,78,93,126],"internet":[13],"slang.":[14],"Traditional":[15],"single-feature":[16],"models":[17],"may":[18],"not":[19],"capturing":[20],"this":[21,53],"diversity":[22],"comprehensively":[23],"in":[25,52],"effectively":[26],"discerning":[27],"the":[28,118,133,138,176,191],"usage":[29],"sentiment":[31,64,73,80,121,196],"words,":[32],"language":[33],"structure,":[34],"contextual":[36,143],"information.":[37],"Consequently,":[38],"they":[39],"tend":[40],"to":[41,89,107,116,136,161,189],"suboptimal":[43],"performance,":[44],"particularly":[45],"when":[46],"dealing":[47],"with":[48,198],"informally":[49],"expressed":[50],"datasets":[51],"domain.":[54],"This":[55],"study":[56],"introduces":[57],"novel":[59],"approach":[60],"for":[61,201],"classification":[65],"that":[66,141,156],"leverages":[67],"multi-dimensional":[68],"(common":[69],"text":[70],"features":[71,140],"features)":[74],"multi-granularity":[76],"(word-level":[77],"character-level)":[79],"features.":[81],"To":[82],"achieve":[83],"this,":[84],"we":[85,99],"first":[86],"employ":[87],"word2vec":[88],"train":[90],"separate":[91],"word-level":[92],"character-level":[94],"embedding":[95],"vectors.":[96],"And":[97],"then,":[98],"introduce":[100],"LSTM":[101,134],"(Long":[102],"Short-Term":[103],"Memory)":[104],"neural":[105],"networks":[106],"capture":[108,137],"sentence-level":[109],"representations.":[110],"Additionally,":[111],"self-attention":[112],"mechanisms":[113],"are":[114],"incorporated":[115],"assess":[117],"importance":[119],"words":[122],"at":[123],"both":[124],"word":[125],"character":[127],"levels,":[128],"finally":[129],"integrating":[130],"them":[131],"into":[132],"network":[135],"sentimental":[139],"incorporate":[142],"semantics.":[144],"We":[145],"conduct":[146],"experiments":[147],"on":[148,164,168],"two":[149],"different":[150],"datasets.":[151,166],"Our":[152,182],"experimental":[153],"results":[154],"reveal":[155],"our":[157,179],"method":[158,184],"performs":[159],"comparably":[160],"competing":[162],"approaches":[163],"high-quality":[165],"However,":[167],"lower-quality":[169],"datasets,":[170],"it":[171],"outperforms":[172],"other":[173],"methods,":[174],"demonstrating":[175],"robustness":[177],"proposed":[180],"approach.":[181],"innovative":[183],"provides":[185],"an":[186],"effective":[187],"solution":[188],"address":[190],"challenges":[192],"classification,":[197],"wide-ranging":[199],"potential":[200],"practical":[202],"applications.":[203]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-04-17T18:11:37.981687","created_date":"2025-10-10T00:00:00"}
