{"id":"https://openalex.org/W4327977221","doi":"https://doi.org/10.1142/s021964922350003x","title":"A Comparative Review of Sentimental Analysis Using Machine Learning and Deep Learning Approaches","display_name":"A Comparative Review of Sentimental Analysis Using Machine Learning and Deep Learning Approaches","publication_year":2023,"publication_date":"2023-03-18","ids":{"openalex":"https://openalex.org/W4327977221","doi":"https://doi.org/10.1142/s021964922350003x"},"language":"en","primary_location":{"id":"doi:10.1142/s021964922350003x","is_oa":false,"landing_page_url":"https://doi.org/10.1142/s021964922350003x","pdf_url":null,"source":{"id":"https://openalex.org/S30163770","display_name":"Journal of Information & Knowledge Management","issn_l":"0219-6492","issn":["0219-6492","1793-6926"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319815","host_organization_name":"World Scientific","host_organization_lineage":["https://openalex.org/P4310319815"],"host_organization_lineage_names":["World Scientific"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Information & Knowledge Management","raw_type":"journal-article"},"type":"review","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/A5085105044","display_name":"Archana Nagelli","orcid":"https://orcid.org/0009-0003-1780-3803"},"institutions":[{"id":"https://openalex.org/I876193797","display_name":"Vellore Institute of Technology University","ror":"https://ror.org/00qzypv28","country_code":"IN","type":"education","lineage":["https://openalex.org/I876193797"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Archana Nagelli","raw_affiliation_strings":["Department of Computer Science and Engineering, Sreenidhi Institute of Science & Technology, Hyderabad 501301, Telangana, India","School of Computer Science and Engineering, Vellore Institute of Technology, Chennai Campus, Chennai 600127, Tamil Nadu, India"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Sreenidhi Institute of Science & Technology, Hyderabad 501301, Telangana, India","institution_ids":[]},{"raw_affiliation_string":"School of Computer Science and Engineering, Vellore Institute of Technology, Chennai Campus, Chennai 600127, Tamil Nadu, India","institution_ids":["https://openalex.org/I876193797"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5007188987","display_name":"B. Saleena","orcid":null},"institutions":[{"id":"https://openalex.org/I876193797","display_name":"Vellore Institute of Technology University","ror":"https://ror.org/00qzypv28","country_code":"IN","type":"education","lineage":["https://openalex.org/I876193797"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"B. Saleena","raw_affiliation_strings":["School of Computer Science and Engineering, Vellore Institute of Technology, Chennai Campus, Chennai 600127, Tamil Nadu, India"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Vellore Institute of Technology, Chennai Campus, Chennai 600127, Tamil Nadu, India","institution_ids":["https://openalex.org/I876193797"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5007188987"],"corresponding_institution_ids":["https://openalex.org/I876193797"],"apc_list":null,"apc_paid":null,"fwci":1.222,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.82504514,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"22","issue":"03","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9998999834060669,"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/T10664","display_name":"Sentiment Analysis and Opinion Mining","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/T13083","display_name":"Advanced Text Analysis Techniques","score":0.991599977016449,"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/T11326","display_name":"Stock Market Forecasting Methods","score":0.988099992275238,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/sentiment-analysis","display_name":"Sentiment analysis","score":0.8058284521102905},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7618467807769775},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6993297934532166},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6799976825714111},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6119430661201477},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.5245521068572998},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.48676609992980957},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.4788529872894287},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.4730747640132904},{"id":"https://openalex.org/keywords/product","display_name":"Product (mathematics)","score":0.4596559405326843},{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.4412335157394409},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.43563398718833923},{"id":"https://openalex.org/keywords/outcome","display_name":"Outcome (game theory)","score":0.41603347659111023},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.38336849212646484},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.19107192754745483}],"concepts":[{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.8058284521102905},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7618467807769775},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6993297934532166},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6799976825714111},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6119430661201477},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.5245521068572998},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.48676609992980957},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.4788529872894287},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.4730747640132904},{"id":"https://openalex.org/C90673727","wikidata":"https://www.wikidata.org/wiki/Q901718","display_name":"Product (mathematics)","level":2,"score":0.4596559405326843},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.4412335157394409},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.43563398718833923},{"id":"https://openalex.org/C148220186","wikidata":"https://www.wikidata.org/wiki/Q7111912","display_name":"Outcome (game theory)","level":2,"score":0.41603347659111023},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.38336849212646484},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.19107192754745483},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C144237770","wikidata":"https://www.wikidata.org/wiki/Q747534","display_name":"Mathematical economics","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","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":2,"locations":[{"id":"doi:10.1142/s021964922350003x","is_oa":false,"landing_page_url":"https://doi.org/10.1142/s021964922350003x","pdf_url":null,"source":{"id":"https://openalex.org/S30163770","display_name":"Journal of Information & Knowledge Management","issn_l":"0219-6492","issn":["0219-6492","1793-6926"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319815","host_organization_name":"World Scientific","host_organization_lineage":["https://openalex.org/P4310319815"],"host_organization_lineage_names":["World Scientific"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Information & Knowledge Management","raw_type":"journal-article"},{"id":"pmh:oai:RePEc:wsi:jikmxx:v:22:y:2023:i:03:n:s021964922350003x","is_oa":false,"landing_page_url":"http://www.worldscientific.com/doi/abs/10.1142/S021964922350003X","pdf_url":null,"source":{"id":"https://openalex.org/S4306401271","display_name":"RePEc: Research Papers in Economics","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I77793887","host_organization_name":"Federal Reserve Bank of St. Louis","host_organization_lineage":["https://openalex.org/I77793887"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":98,"referenced_works":["https://openalex.org/W16693889","https://openalex.org/W178664605","https://openalex.org/W1098261954","https://openalex.org/W1490308602","https://openalex.org/W1490960179","https://openalex.org/W1572786359","https://openalex.org/W1904892614","https://openalex.org/W1910131649","https://openalex.org/W1985242443","https://openalex.org/W2001569578","https://openalex.org/W2020909954","https://openalex.org/W2044207520","https://openalex.org/W2048120908","https://openalex.org/W2064675550","https://openalex.org/W2066259047","https://openalex.org/W2076063813","https://openalex.org/W2096698681","https://openalex.org/W2121242682","https://openalex.org/W2134684245","https://openalex.org/W2135756832","https://openalex.org/W2137981452","https://openalex.org/W2141631351","https://openalex.org/W2145139979","https://openalex.org/W2168505588","https://openalex.org/W2298612130","https://openalex.org/W2299859864","https://openalex.org/W2304640842","https://openalex.org/W2319834509","https://openalex.org/W2410415416","https://openalex.org/W2585577269","https://openalex.org/W2592249290","https://openalex.org/W2604272474","https://openalex.org/W2604472983","https://openalex.org/W2606902231","https://openalex.org/W2609512471","https://openalex.org/W2611614234","https://openalex.org/W2615497679","https://openalex.org/W2735546984","https://openalex.org/W2744028198","https://openalex.org/W2753840835","https://openalex.org/W2768764839","https://openalex.org/W2774343269","https://openalex.org/W2780698117","https://openalex.org/W2786090428","https://openalex.org/W2791521493","https://openalex.org/W2792317186","https://openalex.org/W2799832028","https://openalex.org/W2801011252","https://openalex.org/W2803201508","https://openalex.org/W2883853499","https://openalex.org/W2888501547","https://openalex.org/W2892181857","https://openalex.org/W2895021214","https://openalex.org/W2897148785","https://openalex.org/W2898836421","https://openalex.org/W2901922204","https://openalex.org/W2910191085","https://openalex.org/W2913550731","https://openalex.org/W2918288435","https://openalex.org/W2952198026","https://openalex.org/W2952669938","https://openalex.org/W2963634205","https://openalex.org/W2963874170","https://openalex.org/W2993843842","https://openalex.org/W2999297255","https://openalex.org/W3000739907","https://openalex.org/W3016463114","https://openalex.org/W3017135828","https://openalex.org/W3023158727","https://openalex.org/W3025405133","https://openalex.org/W3027898822","https://openalex.org/W3036114413","https://openalex.org/W3048399453","https://openalex.org/W3088472506","https://openalex.org/W3100216269","https://openalex.org/W3101155479","https://openalex.org/W3102853424","https://openalex.org/W3118681031","https://openalex.org/W3120188833","https://openalex.org/W3120609252","https://openalex.org/W3125271087","https://openalex.org/W3128535632","https://openalex.org/W3130441769","https://openalex.org/W3140931699","https://openalex.org/W3143835353","https://openalex.org/W3155127099","https://openalex.org/W3163014027","https://openalex.org/W3170219958","https://openalex.org/W4200333562","https://openalex.org/W4214610984","https://openalex.org/W4231805003","https://openalex.org/W4236243640","https://openalex.org/W4244508735","https://openalex.org/W4246705388","https://openalex.org/W4247780151","https://openalex.org/W4280499072","https://openalex.org/W4283381162","https://openalex.org/W4285088909"],"related_works":["https://openalex.org/W4390608645","https://openalex.org/W4247566972","https://openalex.org/W2960264696","https://openalex.org/W3090563135","https://openalex.org/W2497432351","https://openalex.org/W4206777497","https://openalex.org/W2548633793","https://openalex.org/W2941935829","https://openalex.org/W4233347783","https://openalex.org/W2910064364"],"abstract_inverted_index":{"The":[0,16,53,83,152,240],"sentiment":[1,258],"data":[2,59,259],"provides":[3,56],"vital":[4,170],"information":[5,157,171],"about":[6],"the":[7,10,28,48,51,57,87,103,114,120,146,183,208,211,227,235,243,255,263],"feedback":[8,91],"of":[9,18,30,50,61,89,94,116,122,148,155,162,185,238,246,257,267],"user\u2019s":[11],"opinion,":[12],"attitude":[13],"and":[14,21,33,42,65,77,97,128,135,141,150,164,174,193,196,225,250,260,265],"emotions.":[15],"business":[17],"product":[19,125],"development":[20],"digital":[22],"marketing":[23],"teams":[24],"entirely":[25],"depends":[26],"upon":[27],"outcome":[29,147],"these":[31],"sentiments":[32],"they":[34],"apply":[35],"various":[36,69,109,180],"Data":[37],"Mining":[38],"techniques,":[39],"Machine":[40,133,248],"Learning":[41,44,134,137,229,249,252],"Deep":[43,136,228,251],"approaches":[45],"to":[46,144,201,222],"analyse":[47],"depth":[49],"dataset.":[52],"Sentiment":[54,187,268],"Analysis":[55,188],"automatic":[58],"mining":[60],"reviews,":[62],"comments,":[63],"opinions":[64,104,149],"suggestions,":[66],"received":[67],"from":[68],"input":[70],"methods,":[71,224],"including":[72],"text,":[73],"audio":[74],"notes,":[75],"images":[76],"emoticons,":[78],"through":[79],"Natural":[80],"Language":[81],"Processing.":[82],"analysis":[84,154],"assists":[85],"in":[86,92,113,182,207],"classification":[88],"reviewer":[90],"terms":[93],"positive,":[95],"negative":[96],"neutral":[98],"categories.":[99],"In":[100],"this":[101],"study,":[102],"shared":[105,158],"by":[106,159],"individuals":[107,160],"over":[108],"social":[110],"networking":[111],"sites":[112],"case":[115],"any":[117,123,166],"big":[118],"event,":[119],"release":[121],"new":[124],"or":[126],"show":[127],"political":[129],"events":[130],"were":[131],"analysed.":[132],"techniques":[138,181,253],"are":[139],"discussed":[140],"used":[142],"dominantly":[143],"illustrate":[145],"events.":[151],"accurate":[153],"vast":[156],"free":[161],"cost":[163],"without":[165],"influence":[167],"can":[168],"provide":[169],"for":[172,254],"organisations":[173],"management":[175],"authorities.":[176],"This":[177],"review":[178],"analyses":[179],"field":[184],"Aspect-Based":[186],"along":[189],"with":[190,234,262],"their":[191],"features":[192],"research":[194],"scopes":[195],"thus,":[197],"it":[198],"helps":[199],"researchers":[200],"focus":[202],"on":[203],"more":[204],"precise":[205],"works":[206],"future.":[209],"Among":[210],"machine":[212],"learning":[213],"algorithms,":[214],"Random":[215],"Forest":[216],"performed":[217],"much":[218],"better":[219],"as":[220],"compared":[221],"other":[223],"among":[226],"approaches,":[230],"Multichannel":[231],"CNN":[232],"outperformed":[233],"highest":[236],"accuracy":[237],"96.23%.":[239],"paper":[241],"includes":[242],"comparative":[244],"study":[245],"multiple":[247],"evaluation":[256],"concludes":[261],"challenges":[264],"scope":[266],"Analysis.":[269]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}