{"id":"https://openalex.org/W4410756738","doi":"https://doi.org/10.32604/cmc.2025.065566","title":"Chinese DeepSeek: Performance of Various Oversampling Techniques on Public Perceptions Using Natural Language Processing","display_name":"Chinese DeepSeek: Performance of Various Oversampling Techniques on Public Perceptions Using Natural Language Processing","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4410756738","doi":"https://doi.org/10.32604/cmc.2025.065566"},"language":"en","primary_location":{"id":"doi:10.32604/cmc.2025.065566","is_oa":true,"landing_page_url":"https://doi.org/10.32604/cmc.2025.065566","pdf_url":null,"source":{"id":"https://openalex.org/S4210191605","display_name":"Computers, materials & continua/Computers, materials & continua (Print)","issn_l":"1546-2218","issn":["1546-2218","1546-2226"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computers, Materials &amp; Continua","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://doi.org/10.32604/cmc.2025.065566","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5033304270","display_name":"Anees Ara","orcid":"https://orcid.org/0000-0001-8442-211X"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Anees Ara","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047043390","display_name":"Muhammad Mujahid","orcid":"https://orcid.org/0009-0005-5751-5528"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Muhammad Mujahid","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009402710","display_name":"Amal Al-Rasheed","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Amal Al-Rasheed","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113132474","display_name":"Shaha Al-Otaibi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shaha Al-Otaibi","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5094050788","display_name":"Tanzila Saba","orcid":"https://orcid.org/0000-0003-2579-4209"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tanzila Saba","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5033304270"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.7453,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.90471576,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":"84","issue":"2","first_page":"2717","last_page":"2731"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.7394000291824341,"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":0.7394000291824341,"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":0.7167999744415283,"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/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.7031999826431274,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/oversampling","display_name":"Oversampling","score":0.8207917213439941},{"id":"https://openalex.org/keywords/natural","display_name":"Natural (archaeology)","score":0.6632586121559143},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.5741925835609436},{"id":"https://openalex.org/keywords/perception","display_name":"Perception","score":0.5248668193817139},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4961608350276947},{"id":"https://openalex.org/keywords/natural-language","display_name":"Natural language","score":0.48180025815963745},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.37921562790870667},{"id":"https://openalex.org/keywords/linguistics","display_name":"Linguistics","score":0.3377455472946167},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.30087074637413025},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.1359177827835083},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.07994428277015686},{"id":"https://openalex.org/keywords/philosophy","display_name":"Philosophy","score":0.07940801978111267},{"id":"https://openalex.org/keywords/archaeology","display_name":"Archaeology","score":0.06547042727470398}],"concepts":[{"id":"https://openalex.org/C197323446","wikidata":"https://www.wikidata.org/wiki/Q331222","display_name":"Oversampling","level":3,"score":0.8207917213439941},{"id":"https://openalex.org/C2776608160","wikidata":"https://www.wikidata.org/wiki/Q4785462","display_name":"Natural (archaeology)","level":2,"score":0.6632586121559143},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5741925835609436},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.5248668193817139},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4961608350276947},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.48180025815963745},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.37921562790870667},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.3377455472946167},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.30087074637413025},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.1359177827835083},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.07994428277015686},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.07940801978111267},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.06547042727470398},{"id":"https://openalex.org/C2776257435","wikidata":"https://www.wikidata.org/wiki/Q1576430","display_name":"Bandwidth (computing)","level":2,"score":0.0},{"id":"https://openalex.org/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.32604/cmc.2025.065566","is_oa":true,"landing_page_url":"https://doi.org/10.32604/cmc.2025.065566","pdf_url":null,"source":{"id":"https://openalex.org/S4210191605","display_name":"Computers, materials & continua/Computers, materials & continua (Print)","issn_l":"1546-2218","issn":["1546-2218","1546-2226"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computers, Materials &amp; Continua","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.32604/cmc.2025.065566","is_oa":true,"landing_page_url":"https://doi.org/10.32604/cmc.2025.065566","pdf_url":null,"source":{"id":"https://openalex.org/S4210191605","display_name":"Computers, materials & continua/Computers, materials & continua (Print)","issn_l":"1546-2218","issn":["1546-2218","1546-2226"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computers, Materials &amp; Continua","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.5799999833106995,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W2945395591","https://openalex.org/W3166839062","https://openalex.org/W3206749614","https://openalex.org/W4282591925","https://openalex.org/W4365452597","https://openalex.org/W4386377968","https://openalex.org/W4391808171","https://openalex.org/W4394005893","https://openalex.org/W4394897028","https://openalex.org/W4398140700","https://openalex.org/W4399274928","https://openalex.org/W4399275008","https://openalex.org/W4402062370","https://openalex.org/W4406758414","https://openalex.org/W4406991706","https://openalex.org/W4407240066"],"related_works":["https://openalex.org/W2766503024","https://openalex.org/W2781247653","https://openalex.org/W4206637278","https://openalex.org/W4386005305","https://openalex.org/W4386214543","https://openalex.org/W3082051559","https://openalex.org/W1969988626","https://openalex.org/W4226226396","https://openalex.org/W3153750606","https://openalex.org/W4308854837"],"abstract_inverted_index":{"DeepSeek":[0,47],"Chinese":[1],"artificial":[2],"intelligence":[3],"(AI)":[4],"open-source":[5],"model,":[6],"has":[7],"gained":[8],"a":[9,22,33,42,49,90,108],"lot":[10],"of":[11,40,45,100,163,212,255],"attention":[12],"due":[13],"to":[14,65,97,186,201],"its":[15],"economical":[16],"training":[17],"and":[18,58,80,118,134,139,168,183,194,219,224,229,250],"efficient":[19],"inference.":[20],"DeepSeek,":[21,75],"model":[23,161,264],"trained":[24],"on":[25],"large-scale":[26],"reinforcement":[27],"learning":[28,57],"without":[29],"supervised":[30],"fine-tuning":[31],"as":[32],"preliminary":[34],"step,":[35],"demonstrates":[36],"remarkable":[37],"reasoning":[38],"capabilities":[39],"performing":[41],"wide":[43],"range":[44],"tasks.":[46],"is":[48],"prominent":[50],"AI-driven":[51],"chatbot":[52],"that":[53,210,242,261],"assists":[54],"individuals":[55],"in":[56,239],"enhances":[59],"responses":[60],"by":[61],"generating":[62],"insightful":[63],"solutions":[64],"inquiries.":[66],"Users":[67],"possess":[68],"divergent":[69],"viewpoints":[70],"regarding":[71],"advanced":[72],"models":[73],"like":[74],"posting":[76],"both":[77,243],"their":[78,247],"merits":[79],"shortcomings":[81],"across":[82],"several":[83],"social":[84],"media":[85],"platforms.":[86],"This":[87],"research":[88],"presents":[89],"new":[91],"framework":[92],"for":[93],"predicting":[94],"public":[95,203],"sentiment":[96,135],"evaluate":[98],"perceptions":[99,208,249],"DeepSeek.":[101,206],"To":[102,174],"transform":[103],"the":[104,127,130,140,146,150,154,213,233,262],"unstructured":[105],"data":[106,152],"into":[107],"suitable":[109],"manner,":[110],"we":[111,125,144,177],"initially":[112],"collect":[113],"DeepSeek-related":[114],"tweets":[115,128],"from":[116,149],"Twitter":[117],"subsequently":[119],"implement":[120],"various":[121],"preprocessing":[122],"methods.":[123],"Subsequently,":[124],"annotated":[126],"utilizing":[129,153],"Valence":[131],"Aware":[132],"Dictionary":[133],"Reasoning":[136],"(VADER)":[137],"methodology":[138],"lexicon-driven":[141],"TextBlob.":[142],"Next,":[143],"classified":[145],"attitudes":[147],"obtained":[148],"purified":[151],"proposed":[155,159,263],"hybrid":[156,160],"model.":[157],"The":[158,207,236,258],"consists":[162],"long-term,":[164],"short-term":[165],"memory":[166],"(LSTM)":[167],"bidirectional":[169],"gated":[170],"recurrent":[171],"units":[172,185],"(BiGRU).":[173],"strengthen":[175],"it,":[176],"include":[178],"multi-head":[179],"attention,":[180],"regularizer":[181],"activation,":[182],"dropout":[184],"enhance":[187],"performance.":[188],"Topic":[189],"modeling":[190],"employing":[191],"KMeans":[192],"clustering":[193],"Latent":[195],"Dirichlet":[196],"Allocation":[197],"(LDA),":[198],"was":[199],"utilized":[200],"analyze":[202],"behavior":[204],"concerning":[205],"demonstrate":[209],"82.5%":[211],"people":[214],"are":[215],"positive,":[216,226],"15.2%":[217],"negative,":[218,228],"2.3%":[220],"neutral":[221,231],"using":[222,232],"TextBlob,":[223],"82.8%":[225],"16.1%":[227],"1.2%":[230],"VADER":[234],"analysis.":[235],"slight":[237],"difference":[238],"results":[240,259],"ensures":[241],"analyses":[244],"concur":[245],"with":[246],"overall":[248],"may":[251],"have":[252],"distinct":[253],"views":[254],"language":[256],"peculiarities.":[257],"indicate":[260],"surpassed":[265],"previous":[266],"state-of-the-art":[267],"approaches.":[268]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-03-12T08:34:05.389933","created_date":"2025-10-10T00:00:00"}
