{"id":"https://openalex.org/W3168865200","doi":"https://doi.org/10.1109/icai52203.2021.9445207","title":"US Based COVID-19 Tweets Sentiment Analysis Using TextBlob and Supervised Machine Learning Algorithms","display_name":"US Based COVID-19 Tweets Sentiment Analysis Using TextBlob and Supervised Machine Learning Algorithms","publication_year":2021,"publication_date":"2021-04-05","ids":{"openalex":"https://openalex.org/W3168865200","doi":"https://doi.org/10.1109/icai52203.2021.9445207","mag":"3168865200"},"language":"en","primary_location":{"id":"doi:10.1109/icai52203.2021.9445207","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icai52203.2021.9445207","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Conference on Artificial Intelligence (ICAI)","raw_type":"proceedings-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/A5078898508","display_name":"Rashid Khan","orcid":"https://orcid.org/0000-0002-2410-044X"},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Rashid Khan","raw_affiliation_strings":["University of Science and Technology, Hefei, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Science and Technology, Hefei, China","institution_ids":["https://openalex.org/I126520041"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058941449","display_name":"Furqan Rustam","orcid":"https://orcid.org/0000-0001-8403-1047"},"institutions":[{"id":"https://openalex.org/I4210102737","display_name":"Khwaja Fareed University of Engineering and Information Technology","ror":"https://ror.org/0161dyt30","country_code":"PK","type":"education","lineage":["https://openalex.org/I4210102737"]}],"countries":["PK"],"is_corresponding":false,"raw_author_name":"Furqan Rustam","raw_affiliation_strings":["Department of CS KFUEIT, Rahim Yar Khan, Pakistan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of CS KFUEIT, Rahim Yar Khan, Pakistan","institution_ids":["https://openalex.org/I4210102737"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070702816","display_name":"Khadija Kanwal","orcid":"https://orcid.org/0009-0001-2300-9636"},"institutions":[{"id":"https://openalex.org/I4210126226","display_name":"The Women University Multan","ror":"https://ror.org/035ggvj17","country_code":"PK","type":"education","lineage":["https://openalex.org/I4210126226"]}],"countries":["PK"],"is_corresponding":false,"raw_author_name":"Khadija Kanwal","raw_affiliation_strings":["Department of CS, Women University, Multan, Pakistan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of CS, Women University, Multan, Pakistan","institution_ids":["https://openalex.org/I4210126226"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087270783","display_name":"Arif Mehmood","orcid":"https://orcid.org/0000-0001-5822-4005"},"institutions":[{"id":"https://openalex.org/I174731842","display_name":"Islamia University of Bahawalpur","ror":"https://ror.org/002rc4w13","country_code":"PK","type":"education","lineage":["https://openalex.org/I174731842"]}],"countries":["PK"],"is_corresponding":false,"raw_author_name":"Arif Mehmood","raw_affiliation_strings":["Department of CS & IT, The Islamia University of Bahawalpur, Punjab, Pakistan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of CS & IT, The Islamia University of Bahawalpur, Punjab, Pakistan","institution_ids":["https://openalex.org/I174731842"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5044080887","display_name":"Gyu Sang Choi","orcid":"https://orcid.org/0000-0002-0854-768X"},"institutions":[{"id":"https://openalex.org/I55240360","display_name":"Yeungnam University","ror":"https://ror.org/05yc6p159","country_code":"KR","type":"education","lineage":["https://openalex.org/I55240360"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Gyu Sang Choi","raw_affiliation_strings":["Department of Information & Communication Engineering, Yeungnam University, Gyeongbuk, Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Information & Communication Engineering, Yeungnam University, Gyeongbuk, Korea","institution_ids":["https://openalex.org/I55240360"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":7.1366,"has_fulltext":false,"cited_by_count":53,"citation_normalized_percentile":{"value":0.97516695,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"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":0.9879999756813049,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.987500011920929,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.7958850264549255},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.7722749710083008},{"id":"https://openalex.org/keywords/sentiment-analysis","display_name":"Sentiment analysis","score":0.6792651414871216},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.6768141388893127},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6748074293136597},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.6586096286773682},{"id":"https://openalex.org/keywords/gradient-boosting","display_name":"Gradient boosting","score":0.6112915277481079},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.4921078085899353},{"id":"https://openalex.org/keywords/lexical-analysis","display_name":"Lexical analysis","score":0.4692649245262146},{"id":"https://openalex.org/keywords/tf\u2013idf","display_name":"tf\u2013idf","score":0.4541683495044708},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.4154321253299713},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.3726102113723755}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7958850264549255},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.7722749710083008},{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.6792651414871216},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.6768141388893127},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6748074293136597},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.6586096286773682},{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.6112915277481079},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.4921078085899353},{"id":"https://openalex.org/C176982825","wikidata":"https://www.wikidata.org/wiki/Q835922","display_name":"Lexical analysis","level":2,"score":0.4692649245262146},{"id":"https://openalex.org/C81758059","wikidata":"https://www.wikidata.org/wiki/Q796584","display_name":"tf\u2013idf","level":3,"score":0.4541683495044708},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.4154321253299713},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3726102113723755},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icai52203.2021.9445207","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icai52203.2021.9445207","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Conference on Artificial Intelligence (ICAI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7900000214576721,"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":48,"referenced_works":["https://openalex.org/W1595459570","https://openalex.org/W1678356000","https://openalex.org/W1873332500","https://openalex.org/W1964613733","https://openalex.org/W1988195734","https://openalex.org/W2008056655","https://openalex.org/W2025831430","https://openalex.org/W2056132907","https://openalex.org/W2074975950","https://openalex.org/W2088794999","https://openalex.org/W2110278938","https://openalex.org/W2134932622","https://openalex.org/W2143017621","https://openalex.org/W2145955806","https://openalex.org/W2150874198","https://openalex.org/W2157052295","https://openalex.org/W2191866459","https://openalex.org/W2216946510","https://openalex.org/W2244750928","https://openalex.org/W2334175399","https://openalex.org/W2431574495","https://openalex.org/W2489406233","https://openalex.org/W2513839347","https://openalex.org/W2599113592","https://openalex.org/W2725799220","https://openalex.org/W2786540448","https://openalex.org/W2792883466","https://openalex.org/W2914537577","https://openalex.org/W2949998441","https://openalex.org/W2963593575","https://openalex.org/W2988412621","https://openalex.org/W3029209828","https://openalex.org/W3033356006","https://openalex.org/W3131504710","https://openalex.org/W3146306708","https://openalex.org/W3208602520","https://openalex.org/W4205184193","https://openalex.org/W4206233629","https://openalex.org/W4285719527","https://openalex.org/W4287767839","https://openalex.org/W6635262639","https://openalex.org/W6637404493","https://openalex.org/W6639175750","https://openalex.org/W6690901545","https://openalex.org/W6736575291","https://openalex.org/W6764146914","https://openalex.org/W6778023125","https://openalex.org/W6807224799"],"related_works":["https://openalex.org/W2967733078","https://openalex.org/W3204430031","https://openalex.org/W3137904399","https://openalex.org/W4310492845","https://openalex.org/W2885778889","https://openalex.org/W2766514146","https://openalex.org/W2885516856","https://openalex.org/W3158961892","https://openalex.org/W4289703016","https://openalex.org/W4310224730"],"abstract_inverted_index":{"In":[0,128],"December":[1],"2019,":[2],"a":[3,58,66,129],"highly":[4],"contagious":[5],"disease,":[6],"Coronavirus":[7],"disease":[8,18],"2019":[9],"(COVID-19)":[10],"was":[11,85,236],"first":[12],"detected":[13],"in":[14,213],"Wuhan,":[15],"China.":[16],"The":[17,252],"has":[19,30],"spread":[20],"to":[21,32,41,51,60,94,106,113,147,170,238],"212":[22],"countries":[23],"and":[24,56,76,139,163,212,223],"territories":[25],"worldwide.":[26],"While":[27],"this":[28,49,214],"epidemic":[29],"continued":[31],"infect":[33],"millions":[34],"of":[35,70,206,227,248,254],"people,":[36],"several":[37],"nations":[38],"have":[39],"resorted":[40],"complete":[42],"lockdowns.":[43],"People":[44],"took":[45],"social":[46],"networks":[47],"during":[48],"shutdown":[50],"share":[52],"their":[53],"opinions,":[54],"feelings,":[55],"find":[57,102],"way":[59],"calm":[61],"down.":[62],"This":[63,81,195,234],"study":[64,197],"proposed":[65],"US-based":[67,82],"sentiment":[68],"analysis":[69,79,235],"the":[71,77,103,122,126,152,204,207,216,221,241,249,255,258],"tweets":[72,83],"using":[73,109,185],"machine":[74,166,209,219],"learning":[75,210],"lexicon":[78],"approach.":[80],"dataset":[84],"collected":[86],"by":[87,246],"RStudio":[88],"software":[89],"from":[90],"30":[91],"January":[92],"2020":[93],"10th":[95],"May":[96],"2020,":[97],"contains":[98],"11858":[99],"tweets.":[100,127],"We":[101],"label":[104],"corresponding":[105],"each":[107],"tweet":[108],"TextBlob,":[110],"that":[111],"is":[112,183,243],"say,":[114],"positive,":[115,175],"negative,":[116,176],"or":[117,177],"neutral.":[118,178],"To":[119],"clean":[120],"up":[121],"facts":[123],"we":[124],"pre-process":[125],"later":[130],"step,":[131],"different":[132],"feature":[133],"techniques":[134],"such":[135],"as":[136,173],"bag-of-words":[137],"(BoW)":[138],"term":[140],"frequency-inverse":[141],"document":[142],"frequency":[143],"(TF-IDF)":[144],"are":[145,168],"used":[146,169],"preserve":[148],"expressive":[149],"information.":[150],"Finally,":[151],"random":[153],"forest,":[154],"gradient":[155,217],"boosting":[156,218],"machine,":[157],"extra":[158],"tree":[159],"classifier,":[160],"logistic":[161],"regression,":[162],"support":[164],"vector":[165],"models":[167,211],"categorize":[171],"beliefs":[172],"being":[174,244],"Our":[179],"suggested":[180],"pipeline":[181],"output":[182],"assessed":[184],"accuracy,":[186],"precision,":[187],"recall":[188],"&":[189],"F":[190],"<inf":[191],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[192],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">1</inf>":[193],"score.":[194],"research":[196],"shows":[198],"how":[199,240],"TF-IDF":[200,232],"features":[201],"can":[202],"increase":[203],"performance":[205],"supervised":[208],"work,":[215],"outperforms":[220],"others":[222],"achieves":[224],"high":[225],"accuracy":[226],"96%":[228],"when":[229],"paired":[230],"with":[231],"features.":[233],"done":[237],"analyze":[239],"situation":[242],"handled":[245],"citizens":[247],"United":[250],"States.":[251],"results":[253],"experiments":[256],"validate":[257],"approach\u2019s":[259],"effectiveness.":[260]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":13},{"year":2023,"cited_by_count":20},{"year":2022,"cited_by_count":17},{"year":2021,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}