{"id":"https://openalex.org/W2792178713","doi":"https://doi.org/10.7287/peerj.preprints.26618v1","title":"Unbalanced sentiment classification: an assessment of ANN in the context of sampling the majority class","display_name":"Unbalanced sentiment classification: an assessment of ANN in the context of sampling the majority class","publication_year":2018,"publication_date":"2018-03-05","ids":{"openalex":"https://openalex.org/W2792178713","doi":"https://doi.org/10.7287/peerj.preprints.26618v1","mag":"2792178713"},"language":"en","primary_location":{"id":"doi:10.7287/peerj.preprints.26618v1","is_oa":true,"landing_page_url":"https://doi.org/10.7287/peerj.preprints.26618v1","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":null,"raw_type":"posted-content"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.7287/peerj.preprints.26618v1","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5024874255","display_name":"Rodrigo Moraes","orcid":null},"institutions":[{"id":"https://openalex.org/I61722147","display_name":"Universidade do Vale do Rio dos Sinos","ror":"https://ror.org/05ctmmy43","country_code":"BR","type":"education","lineage":["https://openalex.org/I61722147"]}],"countries":["BR"],"is_corresponding":true,"raw_author_name":"Rodrigo Moraes","raw_affiliation_strings":["Unidade de Gradua\u00e7\u00e3o, Universidade Vale do Rio dos  Sinos, S\u00e3o Leopoldo, RS, Brazil"],"affiliations":[{"raw_affiliation_string":"Unidade de Gradua\u00e7\u00e3o, Universidade Vale do Rio dos  Sinos, S\u00e3o Leopoldo, RS, Brazil","institution_ids":["https://openalex.org/I61722147"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030595984","display_name":"Jo\u00e3o Francisco Valiati","orcid":"https://orcid.org/0000-0001-8870-1909"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jo\u00e3o Francisco Valiati","raw_affiliation_strings":["Artificial Intelligence Engineers, Porto Alegre, RS, Brazil"],"affiliations":[{"raw_affiliation_string":"Artificial Intelligence Engineers, Porto Alegre, RS, Brazil","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5053890494","display_name":"Wilson Pires Gavi\u00e3o Neto","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wilson Pires Gavi\u00e3o Neto","raw_affiliation_strings":["School of Engeneering & IT, Centro Universit\u00e1rio Ritter dos Reis, Porto Alegre, Rio Grande do Sul, Brazil"],"affiliations":[{"raw_affiliation_string":"School of Engeneering & IT, Centro Universit\u00e1rio Ritter dos Reis, Porto Alegre, Rio Grande do Sul, Brazil","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5024874255"],"corresponding_institution_ids":["https://openalex.org/I61722147"],"apc_list":null,"apc_paid":null,"fwci":0.1629,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.55872517,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"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":0.9991999864578247,"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":0.9991999864578247,"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":0.9904999732971191,"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/T11644","display_name":"Spam and Phishing Detection","score":0.9814000129699707,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/undersampling","display_name":"Undersampling","score":0.9084765911102295},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7670493721961975},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.7381656169891357},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7371845841407776},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.7337021827697754},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5471256971359253},{"id":"https://openalex.org/keywords/oversampling","display_name":"Oversampling","score":0.5145604014396667},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.4921797811985016},{"id":"https://openalex.org/keywords/weighting","display_name":"Weighting","score":0.48650091886520386},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.4827972650527954},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.4677121043205261},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.45917394757270813},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4550609588623047}],"concepts":[{"id":"https://openalex.org/C136536468","wikidata":"https://www.wikidata.org/wiki/Q1225894","display_name":"Undersampling","level":2,"score":0.9084765911102295},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7670493721961975},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.7381656169891357},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7371845841407776},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.7337021827697754},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5471256971359253},{"id":"https://openalex.org/C197323446","wikidata":"https://www.wikidata.org/wiki/Q331222","display_name":"Oversampling","level":3,"score":0.5145604014396667},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.4921797811985016},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.48650091886520386},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.4827972650527954},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.4677121043205261},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.45917394757270813},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4550609588623047},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"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/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.0},{"id":"https://openalex.org/C2776257435","wikidata":"https://www.wikidata.org/wiki/Q1576430","display_name":"Bandwidth (computing)","level":2,"score":0.0},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","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:10.7287/peerj.preprints.26618v1","is_oa":true,"landing_page_url":"https://doi.org/10.7287/peerj.preprints.26618v1","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":null,"raw_type":"posted-content"}],"best_oa_location":{"id":"doi:10.7287/peerj.preprints.26618v1","is_oa":true,"landing_page_url":"https://doi.org/10.7287/peerj.preprints.26618v1","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":null,"raw_type":"posted-content"},"sustainable_development_goals":[{"score":0.7799999713897705,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4308469503","https://openalex.org/W32988189","https://openalex.org/W2904737874","https://openalex.org/W2399571531","https://openalex.org/W80466363","https://openalex.org/W4288337828","https://openalex.org/W2947132063","https://openalex.org/W4287816717","https://openalex.org/W3016117175","https://openalex.org/W3199294801"],"abstract_inverted_index":{"Many":[0],"people":[1],"make":[2],"their":[3],"opinions":[4],"available":[5],"on":[6,151],"the":[7,18,66,98,116,133,154,159,163,171,189,228],"Internet":[8],"nowadays,":[9],"and":[10,57,73,149,207],"researchers":[11],"have":[12,32,181],"been":[13,33,87,129],"proposing":[14],"methods":[15,56,111],"to":[16,35,48,54,64,112,188],"automate":[17],"task":[19],"of":[20,70,118,135,156,161,166,173,198,230],"classifying":[21],"textual":[22],"reviews":[23,43],"as":[24,90,124,175,184],"positive":[25,42,167],"or":[26,81,239],"negative.":[27],"Usual":[28],"supervised":[29],"learning":[30,74],"techniques":[31,63,72],"adopted":[34,89,170],"accomplish":[36],"such":[37],"a":[38,91,125,136,176,199],"task.":[39],"In":[40],"practice,":[41],"are":[44,60,194,215],"abundant":[45],"in":[46,94,106,158,196,220],"comparison":[47],"negative's.":[49],"This":[50,140],"context":[51,134,160],"poses":[52],"challenges":[53],"learning-based":[55],"data":[58,148,224],"undersampling/oversampling":[59],"popular":[61,204],"preprocessing":[62],"overcome":[65],"problem.":[67,192],"A":[68],"combination":[69],"sampling":[71,110,138],"methods,":[75],"like":[76],"Artificial":[77],"Neural":[78],"Networks":[79],"(ANN)":[80],"Support":[82],"Vector":[83],"Machines":[84],"(SVM),":[85],"has":[86,102,128],"successfully":[88],"classification":[92,100],"approach":[93],"many":[95],"areas,":[96],"while":[97],"sentiment":[99,126,144],"literature":[101],"not":[103],"explored":[104],"ANN":[105,157,219,236],"studies":[107,180],"that":[108,213],"involve":[109],"balance":[113],"data.":[114],"Even":[115],"performance":[117,155,172],"SVM,":[119],"which":[120],"is":[121],"widely":[122],"used":[123],"learner,":[127],"rarely":[130],"addressed":[131],"under":[132,227],"preceding":[137],"method.":[139],"paper":[141],"addresses":[142],"document-level":[143],"analysis":[145],"with":[146,203],"unbalanced":[147,222],"focus":[150],"empirically":[152],"assessing":[153],"undersampling":[162],"(majority)":[164],"set":[165],"reviews.":[168],"We":[169],"SVM":[174,183,214,238],"baseline,":[177],"since":[178],"some":[179],"indicated":[182,212],"being":[185],"less":[186],"subject":[187],"class":[190],"imbalance":[191],"Results":[193],"produced":[195],"terms":[197],"traditional":[200],"bag-of-words":[201],"model":[202],"feature":[205],"selection":[206],"weighting":[208],"methods.":[209],"Our":[210],"experiments":[211],"more":[216],"stable":[217],"than":[218],"highly":[221],"(80%)":[223],"scenarios.":[225],"However,":[226],"discarding":[229],"information":[231],"generated":[232],"by":[233],"random":[234],"undersampling,":[235],"outperform":[237],"produce":[240],"comparable":[241],"results.":[242]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}