{"id":"https://openalex.org/W3198946509","doi":"https://doi.org/10.3390/info12090374","title":"A Tweet Sentiment Classification Approach Using a Hybrid Stacked Ensemble Technique","display_name":"A Tweet Sentiment Classification Approach Using a Hybrid Stacked Ensemble Technique","publication_year":2021,"publication_date":"2021-09-14","ids":{"openalex":"https://openalex.org/W3198946509","doi":"https://doi.org/10.3390/info12090374","mag":"3198946509"},"language":"en","primary_location":{"id":"doi:10.3390/info12090374","is_oa":true,"landing_page_url":"https://doi.org/10.3390/info12090374","pdf_url":"https://www.mdpi.com/2078-2489/12/9/374/pdf?version=1631606623","source":{"id":"https://openalex.org/S4210219776","display_name":"Information","issn_l":"2078-2489","issn":["2078-2489"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Information","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2078-2489/12/9/374/pdf?version=1631606623","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5007962300","display_name":"Babacar Gaye","orcid":"https://orcid.org/0000-0002-0734-9892"},"institutions":[{"id":"https://openalex.org/I92403157","display_name":"University of Science and Technology Beijing","ror":"https://ror.org/02egmk993","country_code":"CN","type":"education","lineage":["https://openalex.org/I92403157"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Babacar Gaye","raw_affiliation_strings":["School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China","institution_ids":["https://openalex.org/I92403157"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024869142","display_name":"Dezheng Zhang","orcid":"https://orcid.org/0000-0002-3456-5259"},"institutions":[{"id":"https://openalex.org/I92403157","display_name":"University of Science and Technology Beijing","ror":"https://ror.org/02egmk993","country_code":"CN","type":"education","lineage":["https://openalex.org/I92403157"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dezheng Zhang","raw_affiliation_strings":["School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China","institution_ids":["https://openalex.org/I92403157"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103250166","display_name":"Aziguli Wulamu","orcid":"https://orcid.org/0000-0002-3320-8110"},"institutions":[{"id":"https://openalex.org/I92403157","display_name":"University of Science and Technology Beijing","ror":"https://ror.org/02egmk993","country_code":"CN","type":"education","lineage":["https://openalex.org/I92403157"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Aziguli Wulamu","raw_affiliation_strings":["School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China","institution_ids":["https://openalex.org/I92403157"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5103250166"],"corresponding_institution_ids":["https://openalex.org/I92403157"],"apc_list":{"value":1600,"currency":"CHF","value_usd":1782},"apc_paid":{"value":1600,"currency":"CHF","value_usd":1782},"fwci":5.9818,"has_fulltext":true,"cited_by_count":62,"citation_normalized_percentile":{"value":0.96859831,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":"12","issue":"9","first_page":"374","last_page":"374"},"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/T11550","display_name":"Text and Document Classification Technologies","score":0.9987000226974487,"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":0.9969000220298767,"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":0.8015133142471313},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7720990180969238},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6747260689735413},{"id":"https://openalex.org/keywords/sentiment-analysis","display_name":"Sentiment analysis","score":0.612216591835022},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6055455207824707},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.5831542015075684},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.5723598599433899},{"id":"https://openalex.org/keywords/adaboost","display_name":"AdaBoost","score":0.5365267992019653},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.4627174735069275},{"id":"https://openalex.org/keywords/recall","display_name":"Recall","score":0.44643086194992065},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.43703967332839966},{"id":"https://openalex.org/keywords/logistic-regression","display_name":"Logistic regression","score":0.4334059953689575},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4233139157295227},{"id":"https://openalex.org/keywords/conditional-random-field","display_name":"Conditional random field","score":0.42248812317848206},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4110247492790222}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8015133142471313},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7720990180969238},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6747260689735413},{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.612216591835022},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6055455207824707},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.5831542015075684},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.5723598599433899},{"id":"https://openalex.org/C141404830","wikidata":"https://www.wikidata.org/wiki/Q2823869","display_name":"AdaBoost","level":3,"score":0.5365267992019653},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.4627174735069275},{"id":"https://openalex.org/C100660578","wikidata":"https://www.wikidata.org/wiki/Q18733","display_name":"Recall","level":2,"score":0.44643086194992065},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.43703967332839966},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.4334059953689575},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4233139157295227},{"id":"https://openalex.org/C152565575","wikidata":"https://www.wikidata.org/wiki/Q1124538","display_name":"Conditional random field","level":2,"score":0.42248812317848206},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4110247492790222},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","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/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.3390/info12090374","is_oa":true,"landing_page_url":"https://doi.org/10.3390/info12090374","pdf_url":"https://www.mdpi.com/2078-2489/12/9/374/pdf?version=1631606623","source":{"id":"https://openalex.org/S4210219776","display_name":"Information","issn_l":"2078-2489","issn":["2078-2489"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Information","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:14a862e327ed4e668e663bdf0f8ee8fc","is_oa":true,"landing_page_url":"https://doaj.org/article/14a862e327ed4e668e663bdf0f8ee8fc","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Information, Vol 12, Iss 9, p 374 (2021)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/2078-2489/12/9/374/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/info12090374","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Information; Volume 12; Issue 9; Pages: 374","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/info12090374","is_oa":true,"landing_page_url":"https://doi.org/10.3390/info12090374","pdf_url":"https://www.mdpi.com/2078-2489/12/9/374/pdf?version=1631606623","source":{"id":"https://openalex.org/S4210219776","display_name":"Information","issn_l":"2078-2489","issn":["2078-2489"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Information","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.6000000238418579,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3198946509.pdf","grobid_xml":"https://content.openalex.org/works/W3198946509.grobid-xml"},"referenced_works_count":51,"referenced_works":["https://openalex.org/W168039245","https://openalex.org/W1936088391","https://openalex.org/W1988790447","https://openalex.org/W2003303386","https://openalex.org/W2024932032","https://openalex.org/W2031253971","https://openalex.org/W2069587649","https://openalex.org/W2076063813","https://openalex.org/W2120582504","https://openalex.org/W2150874198","https://openalex.org/W2159002562","https://openalex.org/W2197429038","https://openalex.org/W2252215182","https://openalex.org/W2401379394","https://openalex.org/W2417999172","https://openalex.org/W2484828460","https://openalex.org/W2513839347","https://openalex.org/W2606776062","https://openalex.org/W2625338296","https://openalex.org/W2729797398","https://openalex.org/W2768244127","https://openalex.org/W2781487490","https://openalex.org/W2789564651","https://openalex.org/W2797373921","https://openalex.org/W2806345781","https://openalex.org/W2852439030","https://openalex.org/W2894451163","https://openalex.org/W2963119602","https://openalex.org/W2963749793","https://openalex.org/W2964167669","https://openalex.org/W2988412621","https://openalex.org/W2997308257","https://openalex.org/W3011570378","https://openalex.org/W3013466528","https://openalex.org/W3093859549","https://openalex.org/W3098137804","https://openalex.org/W3102434205","https://openalex.org/W3102944297","https://openalex.org/W3109337921","https://openalex.org/W3111468671","https://openalex.org/W3115044086","https://openalex.org/W3127044632","https://openalex.org/W3135620065","https://openalex.org/W3150290404","https://openalex.org/W3164947024","https://openalex.org/W3167335427","https://openalex.org/W3178185228","https://openalex.org/W4238540786","https://openalex.org/W6606829919","https://openalex.org/W6713175058","https://openalex.org/W6753241079"],"related_works":["https://openalex.org/W3006655138","https://openalex.org/W4382315444","https://openalex.org/W3011239835","https://openalex.org/W4312534362","https://openalex.org/W4386970009","https://openalex.org/W3213126983","https://openalex.org/W3185760728","https://openalex.org/W2915047625","https://openalex.org/W4233259193","https://openalex.org/W2944292463"],"abstract_inverted_index":{"With":[0],"the":[1,15,40,46,70,75,87,90,101,105,184,191],"extensive":[2],"availability":[3],"of":[4,17,30,34,39,48,54,74,89,114,199,221],"social":[5],"media":[6],"platforms,":[7],"Twitter":[8],"has":[9,36],"become":[10,37],"a":[11,82,111,128],"significant":[12],"tool":[13],"for":[14,59,67],"acquisition":[16],"peoples\u2019":[18],"views,":[19],"opinions,":[20],"attitudes,":[21],"and":[22,72,95,123,138,171,194,203],"emotions":[23],"towards":[24],"certain":[25],"entities.":[26],"Within":[27],"this":[28],"frame":[29],"reference,":[31],"sentiment":[32,60],"analysis":[33],"tweets":[35,102],"one":[38],"most":[41],"fascinating":[42],"research":[43],"areas":[44],"in":[45,181,197],"field":[47],"natural":[49],"language":[50],"processing.":[51],"A":[52],"variety":[53],"techniques":[55],"have":[56],"been":[57],"devised":[58],"analysis,":[61],"but":[62],"there":[63],"is":[64],"still":[65],"room":[66],"improvement":[68],"where":[69],"accuracy":[71,219],"efficacy":[73],"system":[76],"are":[77],"concerned.":[78],"This":[79],"study":[80],"proposes":[81],"novel":[83],"approach":[84,160,212],"that":[85,209],"exploits":[86],"advantages":[88],"lexical":[91],"dictionary,":[92],"machine":[93,163],"learning,":[94],"deep":[96,178],"learning":[97,164,179],"classifiers.":[98],"We":[99,155,174],"classified":[100],"based":[103],"on":[104,190],"sentiments":[106],"extracted":[107],"by":[108,216],"TextBlob":[109],"using":[110],"stacked":[112],"ensemble":[113],"three":[115],"long":[116],"short-term":[117],"memory":[118],"(LSTM)":[119],"as":[120,127,147,167],"base":[121],"classifiers":[122],"logistic":[124,168],"regression":[125],"(LR)":[126],"meta":[129],"classifier.":[130],"The":[131],"proposed":[132,159,185,211],"model":[133],"proved":[134],"to":[135],"be":[136],"effective":[137],"time-saving":[139],"since":[140],"it":[141],"does":[142],"not":[143],"require":[144],"feature":[145],"extraction,":[146],"LSTM":[148],"extracts":[149],"features":[150],"without":[151],"any":[152],"human":[153],"intervention.":[154],"also":[156,175],"compared":[157],"our":[158,210],"with":[161,183],"conventional":[162],"models":[165,180],"such":[166],"regression,":[169],"AdaBoost,":[170],"random":[172],"forest.":[173],"included":[176],"state-of-the-art":[177,214],"comparison":[182],"model.":[186],"Experiments":[187],"were":[188,195],"conducted":[189],"sentiment140":[192],"dataset":[193],"evaluated":[196],"terms":[198],"accuracy,":[200],"precision,":[201],"recall,":[202],"F1":[204],"Score.":[205],"Empirical":[206],"results":[207,215],"showed":[208],"manifested":[213],"achieving":[217],"an":[218],"score":[220],"99%.":[222]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":12},{"year":2024,"cited_by_count":17},{"year":2023,"cited_by_count":15},{"year":2022,"cited_by_count":14}],"updated_date":"2026-07-23T08:03:31.855105","created_date":"2025-10-10T00:00:00"}
