{"id":"https://openalex.org/W3088462511","doi":"https://doi.org/10.3233/jcm-204613","title":"Data mining and social networks processing method based on support vector machine and k-nearest neighbor","display_name":"Data mining and social networks processing method based on support vector machine and k-nearest neighbor","publication_year":2020,"publication_date":"2020-09-22","ids":{"openalex":"https://openalex.org/W3088462511","doi":"https://doi.org/10.3233/jcm-204613","mag":"3088462511"},"language":"en","primary_location":{"id":"doi:10.3233/jcm-204613","is_oa":false,"landing_page_url":"https://doi.org/10.3233/jcm-204613","pdf_url":null,"source":{"id":"https://openalex.org/S2765058733","display_name":"Journal of Computational Methods in Sciences and Engineering","issn_l":"1472-7978","issn":["1472-7978","1875-8983"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318577","host_organization_name":"IOS Press","host_organization_lineage":["https://openalex.org/P4310318577"],"host_organization_lineage_names":["IOS Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Computational Methods in Sciences and Engineering","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/A5075405190","display_name":"Youli Lu","orcid":null},"institutions":[{"id":"https://openalex.org/I100286613","display_name":"Hunan Institute of Science and Technology","ror":"https://ror.org/044ysd349","country_code":"CN","type":"education","lineage":["https://openalex.org/I100286613"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Youli Lu","raw_affiliation_strings":["School of Information and Communication Engineering, Hunan Institute of Science and Technology, Yueyang, Hunan, China"],"affiliations":[{"raw_affiliation_string":"School of Information and Communication Engineering, Hunan Institute of Science and Technology, Yueyang, Hunan, China","institution_ids":["https://openalex.org/I100286613"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031289854","display_name":"Jintong Li","orcid":"https://orcid.org/0009-0002-0844-5496"},"institutions":[{"id":"https://openalex.org/I100286613","display_name":"Hunan Institute of Science and Technology","ror":"https://ror.org/044ysd349","country_code":"CN","type":"education","lineage":["https://openalex.org/I100286613"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jintong Li","raw_affiliation_strings":["Nanhu College, Hunan Institute of Science and Technology, Yueyang 414006, Hunan, China"],"affiliations":[{"raw_affiliation_string":"Nanhu College, Hunan Institute of Science and Technology, Yueyang 414006, Hunan, China","institution_ids":["https://openalex.org/I100286613"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100619259","display_name":"Zhihe Yang","orcid":"https://orcid.org/0000-0002-5971-1355"},"institutions":[{"id":"https://openalex.org/I100286613","display_name":"Hunan Institute of Science and Technology","ror":"https://ror.org/044ysd349","country_code":"CN","type":"education","lineage":["https://openalex.org/I100286613"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhihe Yang","raw_affiliation_strings":["School of Information and Communication Engineering, Hunan Institute of Science and Technology, Yueyang, Hunan, China"],"affiliations":[{"raw_affiliation_string":"School of Information and Communication Engineering, Hunan Institute of Science and Technology, Yueyang, Hunan, China","institution_ids":["https://openalex.org/I100286613"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012975538","display_name":"Xianfeng Ou","orcid":"https://orcid.org/0000-0003-4419-7362"},"institutions":[{"id":"https://openalex.org/I100286613","display_name":"Hunan Institute of Science and Technology","ror":"https://ror.org/044ysd349","country_code":"CN","type":"education","lineage":["https://openalex.org/I100286613"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xianfeng Ou","raw_affiliation_strings":["School of Information and Communication Engineering, Hunan Institute of Science and Technology, Yueyang, Hunan, China"],"affiliations":[{"raw_affiliation_string":"School of Information and Communication Engineering, Hunan Institute of Science and Technology, Yueyang, Hunan, China","institution_ids":["https://openalex.org/I100286613"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5089766116","display_name":"Wenwu Xie","orcid":"https://orcid.org/0000-0002-2902-6023"},"institutions":[{"id":"https://openalex.org/I100286613","display_name":"Hunan Institute of Science and Technology","ror":"https://ror.org/044ysd349","country_code":"CN","type":"education","lineage":["https://openalex.org/I100286613"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Wenwu Xie","raw_affiliation_strings":["School of Information and Communication Engineering, Hunan Institute of Science and Technology, Yueyang, Hunan, China"],"affiliations":[{"raw_affiliation_string":"School of Information and Communication Engineering, Hunan Institute of Science and Technology, Yueyang, Hunan, China","institution_ids":["https://openalex.org/I100286613"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5012975538","https://openalex.org/A5089766116"],"corresponding_institution_ids":["https://openalex.org/I100286613"],"apc_list":null,"apc_paid":null,"fwci":0.1326,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.54751416,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"21","issue":"2","first_page":"435","last_page":"447"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.991100013256073,"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":0.991100013256073,"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/T13731","display_name":"Advanced Computing and Algorithms","score":0.954200029373169,"subfield":{"id":"https://openalex.org/subfields/3322","display_name":"Urban Studies"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11644","display_name":"Spam and Phishing Detection","score":0.9527999758720398,"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/support-vector-machine","display_name":"Support vector machine","score":0.8249534368515015},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7475857138633728},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5832696557044983},{"id":"https://openalex.org/keywords/sort","display_name":"sort","score":0.5704397559165955},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5549752712249756},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.523682713508606},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4838225245475769},{"id":"https://openalex.org/keywords/k-nearest-neighbors-algorithm","display_name":"k-nearest neighbors algorithm","score":0.4269358515739441},{"id":"https://openalex.org/keywords/precision-and-recall","display_name":"Precision and recall","score":0.4115546941757202},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3647322654724121},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.11220559477806091}],"concepts":[{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.8249534368515015},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7475857138633728},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5832696557044983},{"id":"https://openalex.org/C88548561","wikidata":"https://www.wikidata.org/wiki/Q347599","display_name":"sort","level":2,"score":0.5704397559165955},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5549752712249756},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.523682713508606},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4838225245475769},{"id":"https://openalex.org/C113238511","wikidata":"https://www.wikidata.org/wiki/Q1071612","display_name":"k-nearest neighbors algorithm","level":2,"score":0.4269358515739441},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.4115546941757202},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3647322654724121},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.11220559477806091}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.3233/jcm-204613","is_oa":false,"landing_page_url":"https://doi.org/10.3233/jcm-204613","pdf_url":null,"source":{"id":"https://openalex.org/S2765058733","display_name":"Journal of Computational Methods in Sciences and Engineering","issn_l":"1472-7978","issn":["1472-7978","1875-8983"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318577","host_organization_name":"IOS Press","host_organization_lineage":["https://openalex.org/P4310318577"],"host_organization_lineage_names":["IOS Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Computational Methods in Sciences and Engineering","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.4399999976158142}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W2145149294","https://openalex.org/W2149711657","https://openalex.org/W2154862285","https://openalex.org/W2239756815","https://openalex.org/W2288739150","https://openalex.org/W2306223847","https://openalex.org/W2520563735","https://openalex.org/W2752778006","https://openalex.org/W2791594385","https://openalex.org/W2909149343","https://openalex.org/W2912217870","https://openalex.org/W2947431935","https://openalex.org/W2962677525","https://openalex.org/W2966733006","https://openalex.org/W2995518851","https://openalex.org/W3093914364","https://openalex.org/W6748844422","https://openalex.org/W6785306518"],"related_works":["https://openalex.org/W2361805396","https://openalex.org/W2972254340","https://openalex.org/W2022231341","https://openalex.org/W1805912688","https://openalex.org/W4255476312","https://openalex.org/W2373973507","https://openalex.org/W2090763504","https://openalex.org/W4250902763","https://openalex.org/W2351154965","https://openalex.org/W2357579988"],"abstract_inverted_index":{"OBJECTIVE:":[0],"With":[1],"Sina":[2,46,57],"Weibo":[3,47],"data":[4,71],"as":[5],"the":[6,23,42,52,60,69,74,91,107,120,133,147,155,166,178,183,187,193],"background,":[7],"support":[8],"vector":[9],"machine":[10],"(SVM)":[11],"and":[12,21,27,67,84,94,116,126,152,160,164,189,216],"k-nearest":[13],"neighbor":[14],"(KNN)":[15],"method":[16],"are":[17,87,130,162],"used":[18],"to":[19,34,50,65,72,89,201],"predict":[20],"analyze":[22,68,212],"user\u2019s":[24],"micro-blog":[25,205],"emotion":[26,206],"related":[28],"behavior":[29],"in":[30,56,204],"social":[31],"network,":[32],"hoping":[33],"obtain":[35,51],"rich":[36],"potential":[37],"business":[38],"value.":[39],"METHODS:":[40],"First,":[41],"API":[43],"interface":[44],"of":[45,54,76,124,136,142,150,158,169,186,192],"is":[48,63,144,199],"utilized":[49,64,88],"information":[53],"users":[55],"Weibo;":[58],"then,":[59],"Excel":[61],"software":[62],"sort":[66],"extracted":[70],"extract":[73],"features":[75],"micro-":[77],"blogs":[78],"posted":[79],"by":[80],"users.":[81],"Second,":[82],"SVM":[83,125,151,159],"KNN":[85,127,161],"algorithms":[86,203],"calculate":[90],"weighted":[92],"average":[93,118],"propose":[95],"a":[96],"hybrid":[97],"multi-classifier-based":[98],"Mixed":[99],"Classifier":[100],"Emotion":[101],"Prediction":[102],"Model":[103],"(MCEPM).":[104],"Finally,":[105],"through":[106],"evaluation":[108],"criteria,":[109],"including":[110],"precision":[111],"(P),":[112],"recall":[113],"rate":[114],"(R),":[115],"harmonic":[117],"(F1),":[119],"specific":[121],"experimental":[122],"results":[123,135,185],"weight":[128,148,156],"coefficients":[129,149,157],"compared":[131],"with":[132,146],"prediction":[134,140,167,184],"MCEPM.":[137],"RESULTS:":[138],"The":[139],"effect":[141,168],"MCEPM":[143,170,179,197],"associated":[145],"KNN.":[153],"If":[154],"0.6":[163],"0.4,":[165],"will":[171],"be":[172],"optimal.":[173],"Comprehensive":[174],"analysis":[175],"shows":[176],"that":[177],"model":[180,198],"can":[181,209],"balance":[182],"positive":[188],"negative":[190],"samples":[191],"two":[194],"classifiers.":[195],"CONCLUSION:":[196],"superior":[200],"other":[202],"prediction,":[207],"which":[208],"help":[210],"enterprises":[211],"users\u2019":[213],"product":[214],"inclination":[215],"provide":[217],"accurate":[218],"customer":[219],"service":[220],"requirements":[221],"for":[222],"enterprises.":[223]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}