{"id":"https://openalex.org/W4386124631","doi":"https://doi.org/10.1109/access.2023.3308520","title":"An Adaptive LDA Optimal Topic Number Selection Method in News Topic Identification","display_name":"An Adaptive LDA Optimal Topic Number Selection Method in News Topic Identification","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4386124631","doi":"https://doi.org/10.1109/access.2023.3308520"},"language":"en","primary_location":{"id":"doi:10.1109/access.2023.3308520","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2023.3308520","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10229130.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10229130.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5012760300","display_name":"Mingming Zheng","orcid":null},"institutions":[{"id":"https://openalex.org/I141962983","display_name":"Shanghai University of Engineering Science","ror":"https://ror.org/0557b9y08","country_code":"CN","type":"education","lineage":["https://openalex.org/I141962983"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Mingming Zheng","raw_affiliation_strings":["School of Mathematics, Physics and Statistics, Shanghai University of Engineering Science, Shanghai, China"],"raw_orcid":"https://orcid.org/0009-0002-9084-0603","affiliations":[{"raw_affiliation_string":"School of Mathematics, Physics and Statistics, Shanghai University of Engineering Science, Shanghai, China","institution_ids":["https://openalex.org/I141962983"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023427583","display_name":"Kaizhong Jiang","orcid":null},"institutions":[{"id":"https://openalex.org/I141962983","display_name":"Shanghai University of Engineering Science","ror":"https://ror.org/0557b9y08","country_code":"CN","type":"education","lineage":["https://openalex.org/I141962983"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kaizhong Jiang","raw_affiliation_strings":["School of Mathematics, Physics and Statistics, Shanghai University of Engineering Science, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Mathematics, Physics and Statistics, Shanghai University of Engineering Science, Shanghai, China","institution_ids":["https://openalex.org/I141962983"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102548515","display_name":"Ranhui Xu","orcid":null},"institutions":[{"id":"https://openalex.org/I141962983","display_name":"Shanghai University of Engineering Science","ror":"https://ror.org/0557b9y08","country_code":"CN","type":"education","lineage":["https://openalex.org/I141962983"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ranhui Xu","raw_affiliation_strings":["School of Mathematics, Physics and Statistics, Shanghai University of Engineering Science, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Mathematics, Physics and Statistics, Shanghai University of Engineering Science, Shanghai, China","institution_ids":["https://openalex.org/I141962983"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5061547394","display_name":"Lulu Qi","orcid":null},"institutions":[{"id":"https://openalex.org/I141962983","display_name":"Shanghai University of Engineering Science","ror":"https://ror.org/0557b9y08","country_code":"CN","type":"education","lineage":["https://openalex.org/I141962983"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lulu Qi","raw_affiliation_strings":["School of Mathematics, Physics and Statistics, Shanghai University of Engineering Science, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Mathematics, Physics and Statistics, Shanghai University of Engineering Science, Shanghai, China","institution_ids":["https://openalex.org/I141962983"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5012760300"],"corresponding_institution_ids":["https://openalex.org/I141962983"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":2.3538,"has_fulltext":true,"cited_by_count":14,"citation_normalized_percentile":{"value":0.90801404,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":"11","issue":null,"first_page":"92273","last_page":"92284"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11550","display_name":"Text and Document Classification Technologies","score":0.9926000237464905,"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":0.9926000237464905,"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/T13910","display_name":"Computational and Text Analysis Methods","score":0.9919000267982483,"subfield":{"id":"https://openalex.org/subfields/3300","display_name":"General Social Sciences"},"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/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9918000102043152,"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/latent-dirichlet-allocation","display_name":"Latent Dirichlet allocation","score":0.8689608573913574},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8315442204475403},{"id":"https://openalex.org/keywords/topic-model","display_name":"Topic model","score":0.7489848732948303},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.630791962146759},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.6234453916549683},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.5383544564247131},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.49199268221855164},{"id":"https://openalex.org/keywords/document-clustering","display_name":"Document clustering","score":0.4757292568683624},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.44194653630256653},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.43931835889816284},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4147852063179016},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.41176098585128784},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3664655089378357}],"concepts":[{"id":"https://openalex.org/C500882744","wikidata":"https://www.wikidata.org/wiki/Q269236","display_name":"Latent Dirichlet allocation","level":3,"score":0.8689608573913574},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8315442204475403},{"id":"https://openalex.org/C171686336","wikidata":"https://www.wikidata.org/wiki/Q3532085","display_name":"Topic model","level":2,"score":0.7489848732948303},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.630791962146759},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6234453916549683},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.5383544564247131},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.49199268221855164},{"id":"https://openalex.org/C177937566","wikidata":"https://www.wikidata.org/wiki/Q4223102","display_name":"Document clustering","level":3,"score":0.4757292568683624},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.44194653630256653},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.43931835889816284},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4147852063179016},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.41176098585128784},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3664655089378357},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","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}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2023.3308520","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2023.3308520","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10229130.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:aefc246b05564dbabcaedc843587f62b","is_oa":true,"landing_page_url":"https://doaj.org/article/aefc246b05564dbabcaedc843587f62b","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 11, Pp 92273-92284 (2023)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2023.3308520","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2023.3308520","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10229130.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4386124631.pdf","grobid_xml":"https://content.openalex.org/works/W4386124631.grobid-xml"},"referenced_works_count":45,"referenced_works":["https://openalex.org/W11244355","https://openalex.org/W212787878","https://openalex.org/W1601068082","https://openalex.org/W1880262756","https://openalex.org/W1969486090","https://openalex.org/W1980862312","https://openalex.org/W1982474113","https://openalex.org/W1983132011","https://openalex.org/W1987295520","https://openalex.org/W2006279708","https://openalex.org/W2093605115","https://openalex.org/W2100163972","https://openalex.org/W2147152072","https://openalex.org/W2147946282","https://openalex.org/W2162317738","https://openalex.org/W2165835468","https://openalex.org/W2238545866","https://openalex.org/W2587488580","https://openalex.org/W2754013857","https://openalex.org/W2791706747","https://openalex.org/W2886234956","https://openalex.org/W2896457183","https://openalex.org/W2942765639","https://openalex.org/W2952370363","https://openalex.org/W2963208184","https://openalex.org/W2983199300","https://openalex.org/W3029708964","https://openalex.org/W3087125265","https://openalex.org/W3096713131","https://openalex.org/W3097779809","https://openalex.org/W3112924188","https://openalex.org/W3203624364","https://openalex.org/W3203818485","https://openalex.org/W4221061284","https://openalex.org/W4233135949","https://openalex.org/W4312973985","https://openalex.org/W4317243171","https://openalex.org/W4323825313","https://openalex.org/W4378672704","https://openalex.org/W6639619044","https://openalex.org/W6646049056","https://openalex.org/W6674922813","https://openalex.org/W6682044806","https://openalex.org/W6755207826","https://openalex.org/W6784674107"],"related_works":["https://openalex.org/W2406417155","https://openalex.org/W1601566332","https://openalex.org/W2058244939","https://openalex.org/W2562500328","https://openalex.org/W2955177613","https://openalex.org/W2348762029","https://openalex.org/W2045714658","https://openalex.org/W2888875874","https://openalex.org/W2370554703","https://openalex.org/W2128744992"],"abstract_inverted_index":{"Nowadays,":[0],"news":[1,13,16,31,108,133,155,294,304,330,345],"text":[2,17,53,109,331,346],"information":[3,153,181],"is":[4,20,46,183,253],"exploding,":[5],"and":[6,10,26,29,39,117,122,151,163,250,291,322,341,358],"people":[7],"need":[8],"more":[9,11],"heterogeneous":[12],"content.":[14],"Therefore,":[15],"topic":[18,54,96,104,134,142,327],"identification":[19,328],"needed":[21],"to":[22,33,36,71,157,296,332],"help":[23,337],"viewers":[24,338],"quickly":[25],"accurately":[27],"screen":[28],"filter":[30],"related":[32],"their":[34],"interests":[35],"save":[37],"time":[38],"energy.":[40],"The":[41,56,192,239,276],"Latent":[42],"Dirichlet":[43],"Allocation(LDA)":[44],"model":[45,70,321],"the":[47,68,78,81,84,91,101,107,118,147,159,187,198,206,209,215,223,229,236,243,257,282,289,298,309,313,319,323,326,343,350,354],"most":[48],"commonly":[49],"used":[50,196],"method":[51,145,170,234,283,351],"for":[52,146,221],"identification.":[55,135],"optimal":[57,141,200,224,244,299,314],"number":[58,86,143,201,225,245,300,315],"of":[59,80,87,94,103,120,132,149,179,202,208,217,226,231,246,293,301,303,311,316,325,329,356],"topics":[60,73,88,227,247,302,317],"must":[61],"be":[62],"specified":[63],"in":[64,74,129,154,168,271,285,318],"advance":[65],"when":[66],"using":[67],"LDA":[69,95,320],"extract":[72],"previous":[75],"studies.":[76],"However,":[77],"selection":[79],"too-large":[82],"or":[83],"too-small":[85],"significantly":[89,254],"impacts":[90],"final":[92,199],"results":[93,194,240,279],"models,":[97],"which":[98,306],"directly":[99],"determines":[100],"quality":[102],"extraction.":[105],"Moreover,":[106],"datasets":[110,156,361],"from":[111,362],"social":[112],"media":[113],"are":[114,165,195],"very":[115],"time-sensitive,":[116],"combination":[119],"temporal":[121,152,164,251,290],"semantic":[123,150,249,292],"modelling":[124],"has":[125],"not":[126],"been":[127],"considered":[128],"past":[130],"studies":[131],"This":[136],"paper":[137,213,287],"proposes":[138],"an":[139],"adaptive":[140],"determination":[144],"fusion":[148,182],"address":[158],"existing":[160],"problems.":[161],"Semantic":[162],"first":[166],"extracted":[167],"this":[169,212,232,286],"as":[171,197,274],"two":[172,188],"different":[173],"views.":[174],"Then,":[175],"density":[176],"peak":[177],"clustering":[178,193],"multi-view":[180],"performed":[184],"based":[185],"on":[186,235],"obtained":[189],"feature":[190],"vectors.":[191],"topics.":[203],"To":[204],"demonstrate":[205],"effectiveness":[207,324],"proposed":[210,284],"method,":[211],"compares":[214],"performance":[216,230],"four":[218,259],"traditional":[219,260],"methods":[220,261],"determining":[222],"with":[228],"paper\u2019s":[233],"public":[237],"datasets.":[238],"show":[241,280],"that":[242,281],"considering":[248],"factors":[252],"better":[255,339],"than":[256],"other":[258,272],"regarding":[262],"F-value,":[263],"PMI":[264],"scores,":[265],"MI":[266],"scores.":[267],"It":[268,335],"performs":[269],"well":[270],"indicators":[273],"well.":[275],"above":[277],"experimental":[278],"combines":[288],"data":[295],"determine":[297],"text,":[305],"can":[307,336],"improve":[308],"accuracy":[310],"selecting":[312],"some":[333],"extent.":[334],"understand":[340],"utilize":[342],"massive":[344],"information.":[347],"In":[348],"addition,":[349],"also":[352],"broadens":[353],"idea":[355],"identifying":[357],"mining":[359],"unique":[360],"multiple":[363],"perspectives.":[364]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":6}],"updated_date":"2026-05-07T13:39:58.223016","created_date":"2025-10-10T00:00:00"}