{"id":"https://openalex.org/W2054335340","doi":"https://doi.org/10.1108/17440081211222591","title":"A new term\u2010weighting scheme for na\u00efve Bayes text categorization","display_name":"A new term\u2010weighting scheme for na\u00efve Bayes text categorization","publication_year":2012,"publication_date":"2012-03-24","ids":{"openalex":"https://openalex.org/W2054335340","doi":"https://doi.org/10.1108/17440081211222591","mag":"2054335340"},"language":"en","primary_location":{"id":"doi:10.1108/17440081211222591","is_oa":false,"landing_page_url":"https://doi.org/10.1108/17440081211222591","pdf_url":null,"source":{"id":"https://openalex.org/S145159096","display_name":"International Journal of Web Information Systems","issn_l":"1744-0084","issn":["1744-0084","1744-0092"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319811","host_organization_name":"Emerald Publishing Limited","host_organization_lineage":["https://openalex.org/P4310319811"],"host_organization_lineage_names":["Emerald Publishing Limited"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Web Information Systems","raw_type":"journal-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/A5033526663","display_name":"Marcelo Mendoza","orcid":"https://orcid.org/0000-0002-7969-6041"},"institutions":[{"id":"https://openalex.org/I75778554","display_name":"Federico Santa Mar\u00eda Technical University","ror":"https://ror.org/05510vn56","country_code":"CL","type":"education","lineage":["https://openalex.org/I75778554"]}],"countries":["CL"],"is_corresponding":true,"raw_author_name":"Marcelo Mendoza","raw_affiliation_strings":["Computer Science Department, Universidad T\u00e9cnica Federico Santa Mar\u00eda, Santiago, Chile"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science Department, Universidad T\u00e9cnica Federico Santa Mar\u00eda, Santiago, Chile","institution_ids":["https://openalex.org/I75778554"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5033526663"],"corresponding_institution_ids":["https://openalex.org/I75778554"],"apc_list":null,"apc_paid":null,"fwci":1.7681,"has_fulltext":false,"cited_by_count":13,"citation_normalized_percentile":{"value":0.86707313,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":"8","issue":"1","first_page":"55","last_page":"72"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11550","display_name":"Text and Document Classification Technologies","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9993000030517578,"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"}},{"id":"https://openalex.org/T12016","display_name":"Web Data Mining and Analysis","score":0.9968000054359436,"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/computer-science","display_name":"Computer science","score":0.821551501750946},{"id":"https://openalex.org/keywords/naive-bayes-classifier","display_name":"Naive Bayes classifier","score":0.7158639430999756},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6494817733764648},{"id":"https://openalex.org/keywords/categorization","display_name":"Categorization","score":0.6260616779327393},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5743546485900879},{"id":"https://openalex.org/keywords/text-categorization","display_name":"Text categorization","score":0.5359789729118347},{"id":"https://openalex.org/keywords/bayes-theorem","display_name":"Bayes' theorem","score":0.4963551163673401},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.4595228135585785},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.4572330415248871},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.436208039522171},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3769601583480835},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.37670645117759705},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.2746295928955078}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.821551501750946},{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.7158639430999756},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6494817733764648},{"id":"https://openalex.org/C94124525","wikidata":"https://www.wikidata.org/wiki/Q912550","display_name":"Categorization","level":2,"score":0.6260616779327393},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5743546485900879},{"id":"https://openalex.org/C2986744138","wikidata":"https://www.wikidata.org/wiki/Q302088","display_name":"Text categorization","level":3,"score":0.5359789729118347},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.4963551163673401},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.4595228135585785},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.4572330415248871},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.436208039522171},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3769601583480835},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.37670645117759705},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.2746295928955078},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1108/17440081211222591","is_oa":false,"landing_page_url":"https://doi.org/10.1108/17440081211222591","pdf_url":null,"source":{"id":"https://openalex.org/S145159096","display_name":"International Journal of Web Information Systems","issn_l":"1744-0084","issn":["1744-0084","1744-0092"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319811","host_organization_name":"Emerald Publishing Limited","host_organization_lineage":["https://openalex.org/P4310319811"],"host_organization_lineage_names":["Emerald Publishing Limited"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Web Information Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/5","score":0.4699999988079071,"display_name":"Gender equality"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W1480376833","https://openalex.org/W1550206324","https://openalex.org/W1567441725","https://openalex.org/W1579410832","https://openalex.org/W1680014149","https://openalex.org/W1827453818","https://openalex.org/W1978394996","https://openalex.org/W1982589161","https://openalex.org/W2014415866","https://openalex.org/W2029970789","https://openalex.org/W2032443316","https://openalex.org/W2035720976","https://openalex.org/W2082729696","https://openalex.org/W2105895401","https://openalex.org/W2112119668","https://openalex.org/W2118020653","https://openalex.org/W2127170504","https://openalex.org/W2135813353","https://openalex.org/W2140336868","https://openalex.org/W2148603752","https://openalex.org/W2150102617","https://openalex.org/W2162006472","https://openalex.org/W4234159986","https://openalex.org/W4238346259","https://openalex.org/W4251326898"],"related_works":["https://openalex.org/W2360898036","https://openalex.org/W2390857744","https://openalex.org/W2133651098","https://openalex.org/W2390698788","https://openalex.org/W2035261173","https://openalex.org/W2138922887","https://openalex.org/W2125109223","https://openalex.org/W2383063829","https://openalex.org/W2082678934","https://openalex.org/W2537862391"],"abstract_inverted_index":{"Purpose":[0],"Automatic":[1],"text":[2,33,40,76,94,118,161,227],"categorization":[3,34,41,77,95,228],"has":[4],"applications":[5],"in":[6],"several":[7,29,130],"domains,":[8],"for":[9,139,245],"example":[10],"e\u2010mail":[11],"spam":[12],"detection,":[13],"sexual":[14],"content":[15],"filtering,":[16],"directory":[17],"maintenance,":[18],"and":[19,155,214],"focused":[20],"crawling,":[21],"among":[22],"others.":[23],"Most":[24],"information":[25,119,235],"retrieval":[26,120,236],"systems":[27],"contain":[28],"components":[30],"which":[31,238],"use":[32],"methods.":[35],"One":[36],"of":[37,50,56,58,66,101,124,132,204,216,242],"the":[38,51,102,115,125,202,205,212,217,232],"first":[39],"methods":[42,149,165,181],"was":[43],"designed":[44,138],"using":[45,135],"a":[46,54,91,223,240],"na\u00efve":[47,59,72,103,133,225],"Bayes":[48,60,73,104,134,226],"representation":[49],"text.":[52],"Currently,":[53],"number":[55],"variations":[57],"have":[61],"been":[62],"discussed.":[63],"The":[64,86,122,142,159,196,208,220],"purpose":[65],"this":[67,140,246],"paper":[68,87,221],"is":[69,127,144],"to":[70,82,108,129,147,178],"evaluate":[71],"approaches":[74],"on":[75,89,98,114,183,231],"introducing":[78,90,167],"new":[79,92,168],"competitive":[80],"extensions":[81,131],"previous":[83],"approaches.":[84],"Design/methodology/approach":[85],"focuses":[88],"Bayesian":[93],"method":[96,126,143,198],"based":[97,113,182,230],"an":[99],"extension":[100],"approach.":[105,218],"Some":[106],"modifications":[107],"document":[109],"representations":[110],"are":[111],"introduced":[112],"well\u2010known":[116,233],"BM25":[117,234],"method.":[121],"performance":[123,174],"compared":[128,145],"benchmark":[136],"datasets":[137],"purpose.":[141],"also":[146,172],"training\u2010based":[148],"such":[150],"as":[151,187],"support":[152,188],"vector":[153,189],"machines":[154,190],"logistic":[156,192],"regression.":[157,193],"Findings":[158],"proposed":[160,197],"categorizer":[162],"outperforms":[163],"state\u2010of\u2010the\u2010art":[164],"without":[166],"computational":[169],"costs.":[170],"It":[171],"achieves":[173],"results":[175,210],"very":[176],"similar":[177],"more":[179],"complex":[180],"criterion":[184],"function":[185],"optimization":[186],"or":[191],"Practical":[194],"implications":[195],"scales":[199],"well":[200],"regarding":[201],"size":[203],"collection":[206],"involved.":[207],"presented":[209],"demonstrate":[211],"efficiency":[213],"effectiveness":[215],"Originality/value":[219],"introduces":[222],"novel":[224],"approach":[229],"model,":[237],"offers":[239],"set":[241],"good":[243],"properties":[244],"problem.":[247]},"counts_by_year":[{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":1},{"year":2016,"cited_by_count":2},{"year":2015,"cited_by_count":2},{"year":2013,"cited_by_count":2}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}