{"id":"https://openalex.org/W2560010249","doi":"https://doi.org/10.1109/ictc.2016.7763455","title":"Enhanced Naive Bayes Classifier for real-time sentiment analysis with SparkR","display_name":"Enhanced Naive Bayes Classifier for real-time sentiment analysis with SparkR","publication_year":2016,"publication_date":"2016-10-01","ids":{"openalex":"https://openalex.org/W2560010249","doi":"https://doi.org/10.1109/ictc.2016.7763455","mag":"2560010249"},"language":"en","primary_location":{"id":"doi:10.1109/ictc.2016.7763455","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ictc.2016.7763455","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 International Conference on Information and Communication Technology Convergence (ICTC)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5044091796","display_name":"Young Gyo Jung","orcid":null},"institutions":[{"id":"https://openalex.org/I848706","display_name":"Sungkyunkwan University","ror":"https://ror.org/04q78tk20","country_code":"KR","type":"education","lineage":["https://openalex.org/I848706"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Young Gyo Jung","raw_affiliation_strings":["College of Software, Sungkyunkwan University, Suwon, Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Software, Sungkyunkwan University, Suwon, Korea","institution_ids":["https://openalex.org/I848706"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100335072","display_name":"Kyung Tae Kim","orcid":"https://orcid.org/0000-0001-6475-6905"},"institutions":[{"id":"https://openalex.org/I848706","display_name":"Sungkyunkwan University","ror":"https://ror.org/04q78tk20","country_code":"KR","type":"education","lineage":["https://openalex.org/I848706"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Kyung Tae Kim","raw_affiliation_strings":["College of Software, Sungkyunkwan University, Suwon, Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Software, Sungkyunkwan University, Suwon, Korea","institution_ids":["https://openalex.org/I848706"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021367414","display_name":"Byungjun Lee","orcid":"https://orcid.org/0000-0001-5334-5238"},"institutions":[{"id":"https://openalex.org/I848706","display_name":"Sungkyunkwan University","ror":"https://ror.org/04q78tk20","country_code":"KR","type":"education","lineage":["https://openalex.org/I848706"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Byungjun Lee","raw_affiliation_strings":["College of Software, Sungkyunkwan University, Suwon, Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Software, Sungkyunkwan University, Suwon, Korea","institution_ids":["https://openalex.org/I848706"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5079532088","display_name":"Hee Yong Youn","orcid":"https://orcid.org/0000-0003-2833-7628"},"institutions":[{"id":"https://openalex.org/I848706","display_name":"Sungkyunkwan University","ror":"https://ror.org/04q78tk20","country_code":"KR","type":"education","lineage":["https://openalex.org/I848706"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Hee Yong Youn","raw_affiliation_strings":["College of Software, Sungkyunkwan University, Suwon, Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Software, Sungkyunkwan University, Suwon, Korea","institution_ids":["https://openalex.org/I848706"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I848706"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":33,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"141","last_page":"146"},"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/T12761","display_name":"Data Stream Mining Techniques","score":0.9990000128746033,"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":0.9977999925613403,"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":0.8443208932876587},{"id":"https://openalex.org/keywords/naive-bayes-classifier","display_name":"Naive Bayes classifier","score":0.807216227054596},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.7049633264541626},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5563691854476929},{"id":"https://openalex.org/keywords/smoothing","display_name":"Smoothing","score":0.5480563044548035},{"id":"https://openalex.org/keywords/sentiment-analysis","display_name":"Sentiment analysis","score":0.544403076171875},{"id":"https://openalex.org/keywords/bayes-classifier","display_name":"Bayes classifier","score":0.5132498741149902},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.47301414608955383},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.47282397747039795},{"id":"https://openalex.org/keywords/bayes-theorem","display_name":"Bayes' theorem","score":0.4686647653579712},{"id":"https://openalex.org/keywords/concept-drift","display_name":"Concept drift","score":0.4279172420501709},{"id":"https://openalex.org/keywords/data-stream","display_name":"Data stream","score":0.4174254536628723},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.26690855622291565},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.12885379791259766},{"id":"https://openalex.org/keywords/data-stream-mining","display_name":"Data stream mining","score":0.08570468425750732},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.0775773823261261}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8443208932876587},{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.807216227054596},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.7049633264541626},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5563691854476929},{"id":"https://openalex.org/C3770464","wikidata":"https://www.wikidata.org/wiki/Q775963","display_name":"Smoothing","level":2,"score":0.5480563044548035},{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.544403076171875},{"id":"https://openalex.org/C185207860","wikidata":"https://www.wikidata.org/wiki/Q17004744","display_name":"Bayes classifier","level":4,"score":0.5132498741149902},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.47301414608955383},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.47282397747039795},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.4686647653579712},{"id":"https://openalex.org/C60777511","wikidata":"https://www.wikidata.org/wiki/Q3045002","display_name":"Concept drift","level":3,"score":0.4279172420501709},{"id":"https://openalex.org/C2778484313","wikidata":"https://www.wikidata.org/wiki/Q1172540","display_name":"Data stream","level":2,"score":0.4174254536628723},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.26690855622291565},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.12885379791259766},{"id":"https://openalex.org/C89198739","wikidata":"https://www.wikidata.org/wiki/Q3079880","display_name":"Data stream mining","level":2,"score":0.08570468425750732},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0775773823261261},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ictc.2016.7763455","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ictc.2016.7763455","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 International Conference on Information and Communication Technology Convergence (ICTC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1565201084","https://openalex.org/W1924689489","https://openalex.org/W1966277025","https://openalex.org/W2043157037","https://openalex.org/W2050213988","https://openalex.org/W2077382854","https://openalex.org/W2097726431","https://openalex.org/W2140785063","https://openalex.org/W2163063229","https://openalex.org/W2166706824","https://openalex.org/W2189465200","https://openalex.org/W2207631730","https://openalex.org/W2212774021","https://openalex.org/W2407706885","https://openalex.org/W2548916437","https://openalex.org/W2582743722","https://openalex.org/W4205184193","https://openalex.org/W6687322159","https://openalex.org/W6688084870","https://openalex.org/W6713395558","https://openalex.org/W6733261796"],"related_works":["https://openalex.org/W2940903377","https://openalex.org/W3108897387","https://openalex.org/W4229924696","https://openalex.org/W4386121812","https://openalex.org/W4307392573","https://openalex.org/W2912132049","https://openalex.org/W2186919162","https://openalex.org/W2981673118","https://openalex.org/W2525764590","https://openalex.org/W2537862391"],"abstract_inverted_index":{"Correct":[0],"and":[1,79,86],"fast":[2],"sentiment":[3,35],"analysis":[4],"of":[5],"continuously":[6],"generated":[7],"data":[8],"such":[9],"as":[10],"Twitter":[11],"message":[12],"is":[13,28],"very":[14],"important":[15],"for":[16,34,54,75,82],"providing":[17],"real-time":[18,56],"customized":[19],"service":[20],"to":[21],"the":[22,29,37,68,77,95,103,110],"users.":[23],"While":[24],"Naive":[25],"Bayes":[26],"Classifier(NBC)":[27],"most":[30],"popular":[31],"classifier":[32],"employed":[33],"analysis,":[36],"existing":[38,104],"studies":[39],"on":[40,45],"it":[41],"have":[42],"been":[43],"based":[44],"single":[46],"server":[47],"environment.":[48],"Consequently,":[49],"they":[50],"are":[51],"not":[52],"adequate":[53],"handling":[55],"stream":[57],"data.":[58],"In":[59],"this":[60],"paper,":[61],"thus,":[62],"we":[63],"propose":[64],"a":[65],"scheme":[66],"adopting":[67],"Laplace":[69],"Smoothing":[70],"technique":[71],"with":[72,91],"Binarized":[73],"NBC":[74],"enhancing":[76],"accuracy,":[78],"employing":[80],"SparkR":[81,111],"speed-up":[83],"via":[84],"distributed":[85],"parallel":[87],"processing.":[88],"Computer":[89],"simulation":[90],"Sentiment140":[92],"reveals":[93],"that":[94,109],"proposed":[96],"approach":[97],"consistently":[98],"allows":[99,113],"higher":[100],"accuracy":[101],"than":[102,116],"schemes.":[105],"It":[106],"also":[107],"identifies":[108],"environment":[112],"faster":[114],"training":[115],"R.":[117]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":2},{"year":2020,"cited_by_count":5},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":5},{"year":2017,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
