{"id":"https://openalex.org/W3004566149","doi":"https://doi.org/10.1080/08839514.2020.1723868","title":"A Methodology Combining Cosine Similarity with Classifier for Text Classification","display_name":"A Methodology Combining Cosine Similarity with Classifier for Text Classification","publication_year":2020,"publication_date":"2020-02-08","ids":{"openalex":"https://openalex.org/W3004566149","doi":"https://doi.org/10.1080/08839514.2020.1723868","mag":"3004566149"},"language":"en","primary_location":{"id":"doi:10.1080/08839514.2020.1723868","is_oa":false,"landing_page_url":"https://doi.org/10.1080/08839514.2020.1723868","pdf_url":null,"source":{"id":"https://openalex.org/S125501549","display_name":"Applied Artificial Intelligence","issn_l":"0883-9514","issn":["0883-9514","1087-6545"],"is_oa":false,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Applied Artificial Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doaj.org/article/01f66dc7e8af4a7482777354d7737ecb","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5004059216","display_name":"Kwang\u2010Il Park","orcid":"https://orcid.org/0000-0002-0199-8090"},"institutions":[{"id":"https://openalex.org/I193775966","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96","country_code":"KR","type":"education","lineage":["https://openalex.org/I193775966"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Kwangil Park","raw_affiliation_strings":["Department of Industrial Engineering, Yonsei University, Seoul, Republic of Korea"],"affiliations":[{"raw_affiliation_string":"Department of Industrial Engineering, Yonsei University, Seoul, Republic of Korea","institution_ids":["https://openalex.org/I193775966"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113441674","display_name":"June Seok Hong","orcid":null},"institutions":[{"id":"https://openalex.org/I28615091","display_name":"Kyonggi University","ror":"https://ror.org/032xf8h46","country_code":"KR","type":"education","lineage":["https://openalex.org/I28615091"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"June Seok Hong","raw_affiliation_strings":["Department of Management Information Systems, Kyonggi University, Gyeonggi-do, Republic of Korea"],"affiliations":[{"raw_affiliation_string":"Department of Management Information Systems, Kyonggi University, Gyeonggi-do, Republic of Korea","institution_ids":["https://openalex.org/I28615091"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5019101777","display_name":"Wooju Kim","orcid":"https://orcid.org/0000-0001-5828-178X"},"institutions":[{"id":"https://openalex.org/I193775966","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96","country_code":"KR","type":"education","lineage":["https://openalex.org/I193775966"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Wooju Kim","raw_affiliation_strings":["Department of Industrial Engineering, Yonsei University, Seoul, Republic of Korea"],"affiliations":[{"raw_affiliation_string":"Department of Industrial Engineering, Yonsei University, Seoul, Republic of Korea","institution_ids":["https://openalex.org/I193775966"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5019101777"],"corresponding_institution_ids":["https://openalex.org/I193775966"],"apc_list":{"value":2195,"currency":"USD","value_usd":2195},"apc_paid":null,"fwci":8.427,"has_fulltext":false,"cited_by_count":128,"citation_normalized_percentile":{"value":0.98105553,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":"34","issue":"5","first_page":"396","last_page":"411"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11550","display_name":"Text and Document Classification Technologies","score":0.9993000030517578,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.9993000030517578,"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":0.9941999912261963,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9919999837875366,"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":0.8150830268859863},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.736211895942688},{"id":"https://openalex.org/keywords/cosine-similarity","display_name":"Cosine similarity","score":0.6991829872131348},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6549451351165771},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.581062376499176},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.5806609392166138},{"id":"https://openalex.org/keywords/tf\u2013idf","display_name":"tf\u2013idf","score":0.5728578567504883},{"id":"https://openalex.org/keywords/random-subspace-method","display_name":"Random subspace method","score":0.5720136165618896},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.518059492111206},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4311540126800537},{"id":"https://openalex.org/keywords/centroid","display_name":"Centroid","score":0.4306827783584595},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.13686630129814148}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8150830268859863},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.736211895942688},{"id":"https://openalex.org/C2780762811","wikidata":"https://www.wikidata.org/wiki/Q1784941","display_name":"Cosine similarity","level":3,"score":0.6991829872131348},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6549451351165771},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.581062376499176},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5806609392166138},{"id":"https://openalex.org/C81758059","wikidata":"https://www.wikidata.org/wiki/Q796584","display_name":"tf\u2013idf","level":3,"score":0.5728578567504883},{"id":"https://openalex.org/C106135958","wikidata":"https://www.wikidata.org/wiki/Q7291993","display_name":"Random subspace method","level":3,"score":0.5720136165618896},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.518059492111206},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4311540126800537},{"id":"https://openalex.org/C146599234","wikidata":"https://www.wikidata.org/wiki/Q511093","display_name":"Centroid","level":2,"score":0.4306827783584595},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.13686630129814148},{"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1080/08839514.2020.1723868","is_oa":false,"landing_page_url":"https://doi.org/10.1080/08839514.2020.1723868","pdf_url":null,"source":{"id":"https://openalex.org/S125501549","display_name":"Applied Artificial Intelligence","issn_l":"0883-9514","issn":["0883-9514","1087-6545"],"is_oa":false,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Applied Artificial Intelligence","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:01f66dc7e8af4a7482777354d7737ecb","is_oa":true,"landing_page_url":"https://doaj.org/article/01f66dc7e8af4a7482777354d7737ecb","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Applied Artificial Intelligence, Vol 34, Iss 5, Pp 396-411 (2020)","raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:doaj.org/article:01f66dc7e8af4a7482777354d7737ecb","is_oa":true,"landing_page_url":"https://doaj.org/article/01f66dc7e8af4a7482777354d7737ecb","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Applied Artificial Intelligence, Vol 34, Iss 5, Pp 396-411 (2020)","raw_type":"article"},"sustainable_development_goals":[{"score":0.47999998927116394,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W1982589161","https://openalex.org/W1999635750","https://openalex.org/W2033569712","https://openalex.org/W2066814510","https://openalex.org/W2102294253","https://openalex.org/W2105591985","https://openalex.org/W2114080886","https://openalex.org/W2153635508","https://openalex.org/W2562036780","https://openalex.org/W2604504584","https://openalex.org/W2610765098","https://openalex.org/W2742408064","https://openalex.org/W2751269451","https://openalex.org/W2913969657","https://openalex.org/W2930957955","https://openalex.org/W4238674207","https://openalex.org/W4285719527"],"related_works":["https://openalex.org/W2320375988","https://openalex.org/W3170122200","https://openalex.org/W1964832275","https://openalex.org/W2389865566","https://openalex.org/W2350878010","https://openalex.org/W3008856892","https://openalex.org/W2407804800","https://openalex.org/W1603777065","https://openalex.org/W2151191523","https://openalex.org/W115238348"],"abstract_inverted_index":{"Text":[0],"Classification":[1],"has":[2],"received":[3],"significant":[4,165],"attention":[5],"in":[6,20,148,159,167,192],"recent":[7],"years":[8],"because":[9],"of":[10,13,108,144,150,175,194],"the":[11,106,109,115,126,142,145,151,161],"proliferation":[12],"digital":[14],"documents":[15],"and":[16,26,39,92,102,111,137,140,183],"is":[17,189],"widely":[18],"used":[19],"various":[21],"applications":[22],"such":[23,98],"as":[24,99,121],"filtering":[25],"recommendation.":[27],"Consequently,":[28],"many":[29],"approaches,":[30,53],"including":[31],"those":[32],"based":[33],"on":[34],"statistical":[35],"theory,":[36],"machine":[37],"learning,":[38],"classifier":[40,195],"performance":[41,143],"improvement,":[42],"have":[43],"been":[44],"proposed":[45],"for":[46,80],"improving":[47,81],"text":[48],"classification":[49],"performance.":[50,82,196],"Among":[51],"these":[52],"centroid-based":[54],"classifier,":[55],"multinomial":[56],"na\u00efve":[57],"bayesian":[58],"(MNB),":[59],"support":[60],"vector":[61],"machines":[62],"(SVM),":[63],"convolutional":[64],"neural":[65],"network":[66],"(CNN)":[67],"are":[68],"commonly":[69],"used.":[70],"In":[71],"this":[72],"paper,":[73],"we":[74,113,155,172],"introduce":[75],"a":[76,89],"cosine":[77,86,119],"similarity-based":[78],"methodology":[79,84],"The":[83],"combines":[85],"similarity":[87,120],"(between":[88],"test":[90],"document":[91,186],"fixed":[93],"categories)":[94],"with":[95,118],"conventional":[96,116],"classifiers":[97,117,128,147,163],"MNB,":[100],"SVM,":[101],"CNN":[103],"to":[104,129],"improve":[105],"accuracy":[107],"classifiers,":[110],"then":[112],"call":[114],"enhanced":[122,127,146,162],"classifiers.":[123],"We":[124],"applied":[125],"famous":[130],"datasets":[131],"\u2013":[132,139],"20NG,":[133],"R8,":[134],"R52,":[135],"Cade12,":[136],"WebKB":[138],"evaluated":[141],"terms":[149,193],"confusion":[152],"matrix\u2019s":[153],"accuracy;":[154],"obtained":[156],"outstanding":[157],"results":[158],"that":[160],"show":[164],"increases":[166],"accuracy.":[168],"Moreover,":[169],"through":[170],"experiments,":[171],"identified":[173],"which":[174],"two":[176],"considered":[177],"knowledge":[178],"representation":[179],"techniques":[180],"(word":[181],"count":[182],"term":[184],"frequency-inverse":[185],"frequency":[187],"(TFIDF))":[188],"more":[190],"suitable":[191]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":36},{"year":2024,"cited_by_count":27},{"year":2023,"cited_by_count":24},{"year":2022,"cited_by_count":20},{"year":2021,"cited_by_count":16},{"year":2020,"cited_by_count":2}],"updated_date":"2026-04-15T08:11:43.952461","created_date":"2025-10-10T00:00:00"}
