{"id":"https://openalex.org/W4412976203","doi":"https://doi.org/10.1142/s0218126625504134","title":"A Deep Learning-Based Framework for Sentiment and Emotion Classification of Social Media Messages During Pandemic Periods","display_name":"A Deep Learning-Based Framework for Sentiment and Emotion Classification of Social Media Messages During Pandemic Periods","publication_year":2025,"publication_date":"2025-07-18","ids":{"openalex":"https://openalex.org/W4412976203","doi":"https://doi.org/10.1142/s0218126625504134"},"language":"en","primary_location":{"id":"doi:10.1142/s0218126625504134","is_oa":false,"landing_page_url":"https://doi.org/10.1142/s0218126625504134","pdf_url":null,"source":{"id":"https://openalex.org/S167602672","display_name":"Journal of Circuits Systems and Computers","issn_l":"0218-1266","issn":["0218-1266","1793-6454"],"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 Circuits, Systems and Computers","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/A5101690866","display_name":"Yu Feng","orcid":"https://orcid.org/0000-0002-9014-5179"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Feng Yu","raw_affiliation_strings":["School of Journalism and Communication, Wuhan University, Wuhan, Hubei 430072, China"],"affiliations":[{"raw_affiliation_string":"School of Journalism and Communication, Wuhan University, Wuhan, Hubei 430072, China","institution_ids":["https://openalex.org/I37461747"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5001910899","display_name":"Jian Ming Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jian Ming Liu","raw_affiliation_strings":["School of Journalism and Communication, Wuhan University, Wuhan, Hubei 430072, China"],"affiliations":[{"raw_affiliation_string":"School of Journalism and Communication, Wuhan University, Wuhan, Hubei 430072, China","institution_ids":["https://openalex.org/I37461747"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5101690866"],"corresponding_institution_ids":["https://openalex.org/I37461747"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.11144216,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"35","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":0.9919999837875366,"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":0.9919999837875366,"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/T11147","display_name":"Misinformation and Its Impacts","score":0.9567000269889832,"subfield":{"id":"https://openalex.org/subfields/3312","display_name":"Sociology and Political Science"},"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/sentiment-analysis","display_name":"Sentiment analysis","score":0.7100731730461121},{"id":"https://openalex.org/keywords/social-media","display_name":"Social media","score":0.6052473187446594},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5455781817436218},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5364421606063843},{"id":"https://openalex.org/keywords/pandemic","display_name":"Pandemic","score":0.49921464920043945},{"id":"https://openalex.org/keywords/emotion-classification","display_name":"Emotion classification","score":0.4514462351799011},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4498556852340698},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.43230724334716797},{"id":"https://openalex.org/keywords/coronavirus-disease-2019","display_name":"Coronavirus disease 2019 (COVID-19)","score":0.4114535450935364},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.38235312700271606},{"id":"https://openalex.org/keywords/cognitive-psychology","display_name":"Cognitive psychology","score":0.3535199761390686},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3323664963245392},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.3285664916038513},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.2099350094795227},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.13113179802894592}],"concepts":[{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.7100731730461121},{"id":"https://openalex.org/C518677369","wikidata":"https://www.wikidata.org/wiki/Q202833","display_name":"Social media","level":2,"score":0.6052473187446594},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5455781817436218},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5364421606063843},{"id":"https://openalex.org/C89623803","wikidata":"https://www.wikidata.org/wiki/Q12184","display_name":"Pandemic","level":5,"score":0.49921464920043945},{"id":"https://openalex.org/C206310091","wikidata":"https://www.wikidata.org/wiki/Q750859","display_name":"Emotion classification","level":2,"score":0.4514462351799011},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4498556852340698},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.43230724334716797},{"id":"https://openalex.org/C3008058167","wikidata":"https://www.wikidata.org/wiki/Q84263196","display_name":"Coronavirus disease 2019 (COVID-19)","level":4,"score":0.4114535450935364},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.38235312700271606},{"id":"https://openalex.org/C180747234","wikidata":"https://www.wikidata.org/wiki/Q23373","display_name":"Cognitive psychology","level":1,"score":0.3535199761390686},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3323664963245392},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.3285664916038513},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.2099350094795227},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.13113179802894592},{"id":"https://openalex.org/C524204448","wikidata":"https://www.wikidata.org/wiki/Q788926","display_name":"Infectious disease (medical specialty)","level":3,"score":0.0},{"id":"https://openalex.org/C142724271","wikidata":"https://www.wikidata.org/wiki/Q7208","display_name":"Pathology","level":1,"score":0.0},{"id":"https://openalex.org/C2779134260","wikidata":"https://www.wikidata.org/wiki/Q12136","display_name":"Disease","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1142/s0218126625504134","is_oa":false,"landing_page_url":"https://doi.org/10.1142/s0218126625504134","pdf_url":null,"source":{"id":"https://openalex.org/S167602672","display_name":"Journal of Circuits Systems and Computers","issn_l":"0218-1266","issn":["0218-1266","1793-6454"],"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 Circuits, Systems and Computers","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","score":0.6299999952316284,"display_name":"Good health and well-being"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W3021303430","https://openalex.org/W3021829213","https://openalex.org/W3035119815","https://openalex.org/W3045669112","https://openalex.org/W3088352144","https://openalex.org/W3096393000","https://openalex.org/W3097996540","https://openalex.org/W3104432484","https://openalex.org/W3108759814","https://openalex.org/W3115248358","https://openalex.org/W3117864197","https://openalex.org/W3118616224","https://openalex.org/W3131552811","https://openalex.org/W3185375861","https://openalex.org/W4296916927","https://openalex.org/W4321486452","https://openalex.org/W4368368482","https://openalex.org/W4376877198","https://openalex.org/W4380714981","https://openalex.org/W4384819716","https://openalex.org/W4385289230","https://openalex.org/W4389610068","https://openalex.org/W4389633740","https://openalex.org/W4390329420","https://openalex.org/W4391661551","https://openalex.org/W4392173753","https://openalex.org/W4393207154","https://openalex.org/W4399486922","https://openalex.org/W4399527576","https://openalex.org/W4401636497","https://openalex.org/W4402260442","https://openalex.org/W4402625337","https://openalex.org/W4403510661","https://openalex.org/W4404292014","https://openalex.org/W4405097447","https://openalex.org/W4408441805"],"related_works":["https://openalex.org/W2748952813","https://openalex.org/W2548633793","https://openalex.org/W3013279174","https://openalex.org/W2941935829","https://openalex.org/W2596247554","https://openalex.org/W3132372214","https://openalex.org/W4224284088","https://openalex.org/W4286571989","https://openalex.org/W2765903680","https://openalex.org/W4317653575"],"abstract_inverted_index":{"Twitter":[0,103,142,250],"is":[1,15,132,152,168,186,332],"an":[2,16],"emerging":[3],"social":[4,88],"media":[5,89],"platform":[6,18],"with":[7],"more":[8],"than":[9,344],"700":[10],"billion":[11],"users":[12],"worldwide.":[13],"It":[14,27],"active":[17],"for":[19,305],"spreading":[20],"information,":[21],"sharing":[22],"opinions,":[23],"expressing":[24],"emotions,":[25],"etc.":[26],"helps":[28],"technical":[29],"researchers":[30,94],"understand":[31,69],"the":[32,56,70,74,107,139,156,160,164,173,201,214,223,240,260,264,301,306,323,327,335],"mental":[33],"ability":[34],"of":[35,62,72,141,175,216,349],"specific":[36],"situations":[37],"during":[38],"pandemic":[39,52],"periods":[40],"such":[41,144,278,352],"as":[42,145,225,279,353],"COVID-19":[43,47],"and":[44,60,99,112,128,150,231,254,276,284,290,294,362],"FLU.":[45],"Recently,":[46],"has":[48,54,76],"been":[49],"a":[50,78],"severe":[51],"that":[53],"affected":[55],"world\u2019s":[57],"economic":[58],"rate,":[59],"lots":[61],"people":[63,81],"have":[64],"lost":[65],"their":[66,85],"lives.":[67],"To":[68],"severity":[71],"COVID-19,":[73],"government":[75,108,307],"announced":[77],"lockdown":[79],"so":[80],"worldwide":[82],"can":[83],"share":[84],"emotions":[86,271],"through":[87],"platforms.":[90],"This":[91],"initiated":[92],"many":[93],"to":[95,105,118,134,154,170,177,196,209,286,317],"research":[96],"sentiment":[97,127,184,217],"analysis":[98],"emotion":[100,129,296],"classification":[101,130,157,180,185,297],"in":[102,200,334,347],"messages":[104],"help":[106],"sectors":[109],"take":[110,319],"necessary":[111],"accurate":[113,120,136],"actions.":[114],"However,":[115],"they":[116,311],"failed":[117],"provide":[119,135],"results.":[121,137,181],"In":[122,159],"this":[123],"paper,":[124],"deep":[125],"learning-based":[126],"(SENTI-EMO)":[131],"proposed":[133,340],"Initially,":[138],"pre-processing":[140],"messages,":[143],"Noise":[146],"removal,":[147],"correction,":[148],"tokenization":[149],"normalization,":[151],"done":[153,187],"improve":[155],"accuracy.":[158,298,329],"noise":[161],"removal":[162],"phase,":[163],"stop":[165],"word":[166],"technique":[167],"used":[169],"learn":[171],"about":[172],"context":[174],"tweets":[176],"enhance":[178,295,326],"further":[179],"After":[182],"pre-processing,":[183],"by":[188,238,258],"using":[189,239,259],"attention-based":[190],"GRU,":[191],"which":[192,212,245,325],"contains":[193],"several":[194],"layers":[195],"extract":[197],"high-level":[198],"features;":[199],"attention":[202],"layer,":[203],"only":[204],"essential":[205],"features":[206,221],"are":[207,236,267,303],"considered":[208],"reduce":[210,287],"redundancy,":[211],"enhances":[213],"accuracy":[215],"classification.":[218],"The":[219,233,330,339],"extracted":[220],"classify":[222],"sentiments":[224,235,266],"strongly":[226],"positive,":[227,228],"negative,":[229],"negative":[230],"neutral.":[232],"classified":[234,268],"clustered":[237,265],"spatiotemporal":[241],"optics":[242],"(STO)":[243],"algorithm,":[244,263],"forms":[246],"clusters":[247],"based":[248,272,321],"on":[249,273,322],"ID,":[251],"geographical":[252],"location,":[253],"time":[255,364],"stamp.":[256],"Then,":[257],"Multi-class":[261],"CatBoost":[262],"into":[269],"different":[270],"various":[274],"topics":[275],"locations,":[277],"fear,":[280],"anger,":[281],"trust,":[282],"joy":[283],"sadness,":[285],"overfitting,":[288],"latency,":[289],"class":[291],"imbalance":[292],"issues":[293],"Finally,":[299],"all":[300],"reports":[302,324],"generated":[304],"sector,":[308],"from":[309],"there":[310],"use":[312],"soft":[313],"actor-critic":[314],"(SAC)":[315],"algorithm":[316],"accurately":[318],"actions":[320],"detection":[328],"process":[331],"implemented":[333],"Python":[336],"3":[337],"tool.":[338],"work":[341],"performs":[342],"better":[343],"existing":[345],"works":[346],"terms":[348],"validation":[350],"metrics":[351],"Accuracy":[354],"(avg-92.04%),":[355],"Precision":[356],"(avg-93.2%),":[357],"Recall":[358],"(avg-92.58%),":[359],"F-score":[360],"(avg-91.8%)":[361],"computation":[363],"(avg-74.8":[365],"ms).":[366]},"counts_by_year":[],"updated_date":"2025-12-21T01:58:51.020947","created_date":"2025-10-10T00:00:00"}