{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,21]],"date-time":"2026-08-21T13:18:10Z","timestamp":1787318290164,"version":"build-2736575974"},"reference-count":46,"publisher":"SAGE Publications","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2023,7,2]]},"abstract":"<jats:p>COVID-19 epidemic is one of the worst disaster which affected people worldwide. It has impacted whole civilization physically, monetarily, and also emotionally. Sentiment analysis is an important step to handle pandemic effectively. In this work, systematic literature review of sentiment analysis of Indian population towards COVID-19 and its vaccination is presented. Recent exiting works are considered from four primary databases including ACM, Web of Science, IEEE Explore, and Scopus. Total 40 publications from January 2020 to August 2022 are selected for systematic review after applying inclusion and exclusion algorithm. Existing works are analyzed in terms of various challenges encountered by the existing authors with collected datasets. It is analyzed that mainly three techniques namely lexical, machine and deep learning are used by various authors for sentiment analysis. Performance of various applied techniques are comparative analyzed. Direction of future research works with recommendations are highlighted.<\/jats:p>","DOI":"10.3233\/jifs-224086","type":"journal-article","created":{"date-parts":[[2023,5,5]],"date-time":"2023-05-05T12:15:04Z","timestamp":1683288904000},"page":"731-742","source":"Crossref","is-referenced-by-count":8,"title":["Analyzing research trends of sentiment analysis and its applications for Coronavirus disease (COVID-19): A systematic review"],"prefix":"10.1177","volume":"45","author":[{"given":"Vipin","family":"Jain","sequence":"first","affiliation":[{"name":"VIT Bhopal University, Madhya Pradesh, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kanchan Lata","family":"Kashyap","sequence":"additional","affiliation":[{"name":"VIT Bhopal University, Madhya Pradesh, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","reference":[{"key":"10.3233\/JIFS-224086_ref1","doi-asserted-by":"crossref","first-page":"e0263787","DOI":"10.1371\/journal.pone.0263787","article-title":"Topical analysis of migration coverage during lockdown in india by mainstream print media","volume":"17","author":"Agarwal","year":"2022","journal-title":"Plos One"},{"key":"10.3233\/JIFS-224086_ref2","doi-asserted-by":"crossref","first-page":"695913","DOI":"10.3389\/fcomm.2021.695913","article-title":"Psychometric analysis and coupling of emotions between statebulletins and twitter in india during covid-19 infodemic","volume":"6","author":"Aggrawal","year":"2021","journal-title":"Frontiers in Communication"},{"key":"10.3233\/JIFS-224086_ref3","doi-asserted-by":"crossref","first-page":"114155","DOI":"10.1016\/j.eswa.2020.114155","article-title":"Sentiment analysis and its applications in fighting covid-19and infectious diseases: A systematic review","volume":"167","author":"Alamoodi","year":"2021","journal-title":"Expert systems with applications"},{"key":"10.3233\/JIFS-224086_ref5","doi-asserted-by":"crossref","first-page":"3199","DOI":"10.3390\/math10173199","article-title":"Role of artificial intelligence for analysis of covid-19 vaccination-related tweets: Opportunities, challenges, and future trends","volume":"10","author":"Aljedaani","year":"2022","journal-title":"Mathematics"},{"key":"10.3233\/JIFS-224086_ref6","doi-asserted-by":"crossref","first-page":"57","DOI":"10.3126\/aet.v1i1.39660","article-title":"Sentiment analysis on covid-19 vaccination tweets using naive bayes and lstm","volume":"1","author":"Aryal","year":"2021","journal-title":"Advances in Engineering and Technology: An International Journal"},{"key":"10.3233\/JIFS-224086_ref7","doi-asserted-by":"crossref","first-page":"106489","DOI":"10.1016\/j.epsr.2020.106489","article-title":"Assessment of stacked unidirectional andbidirectional long short-term memory networks for electricity load forecasting","volume":"187","author":"Atef","year":"2020","journal-title":"Electric Power Systems Research"},{"key":"10.3233\/JIFS-224086_ref8","first-page":"102089","article-title":"Sentiment analysis of nationwidelockdown due to covid 19 outbreak: Evidence from india","volume":"51","author":"Barkur","year":"2020","journal-title":"Asianjournal of psychiatry"},{"key":"10.3233\/JIFS-224086_ref10","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Machine Learning"},{"key":"10.3233\/JIFS-224086_ref14","doi-asserted-by":"crossref","first-page":"106754","DOI":"10.1016\/j.asoc.2020.106754","article-title":"Sentiment analysis of covid-19 tweets by deeplearning classifiers\u2014a study to show how popularity isaffecting accuracy in social media","volume":"97","author":"Chakraborty","year":"2020","journal-title":"Applied Soft Computing"},{"key":"10.3233\/JIFS-224086_ref15","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1177\/0020764020940741","article-title":"Covid-19 pandemic lockdown: Anemotional health perspective of indians on twitter","volume":"67","author":"Chehal","year":"2021","journal-title":"International Journal of Social Psychiatry"},{"key":"10.3233\/JIFS-224086_ref16","doi-asserted-by":"crossref","first-page":"329","DOI":"10.3390\/idr13020032","article-title":"Sentimental analysis ofcovid-19 tweets using deep learning models","volume":"13","author":"Chintalapudi","year":"2021","journal-title":"Infectious Disease Reports"},{"key":"10.3233\/JIFS-224086_ref17","doi-asserted-by":"crossref","unstructured":"Chiny M. , Chihab M. , Bencharef O. and Chihab Y. , Lstm, vader andtf-idf based hybrid sentiment analysis model, InternationalJournal of Advanced Computer Science and Applications 12 (2021).","DOI":"10.14569\/IJACSA.2021.0120730"},{"key":"10.3233\/JIFS-224086_ref20","doi-asserted-by":"crossref","first-page":"1316","DOI":"10.18421\/TEM113-41","article-title":"Sentimentanalysis of covid-19 using multimodal fusion neural networks","volume":"11","author":"Ermatita","year":"2022","journal-title":"TEM Journal"},{"key":"10.3233\/JIFS-224086_ref21","doi-asserted-by":"crossref","first-page":"36645","DOI":"10.1109\/ACCESS.2021.3062875","article-title":"Investigating covid-19 news across fournations: A topic modeling and sentiment analysis approach","volume":"9","author":"Ghasiya","year":"2021","journal-title":"IEEE Access"},{"key":"10.3233\/JIFS-224086_ref22","doi-asserted-by":"crossref","first-page":"S264","DOI":"10.1016\/j.mjafi.2021.06.032","article-title":"Covishield (azdvaccine effectiveness among healthcare and frontline workers of indian armed forces: Interim results of vin-wincohort study","volume":"77","author":"Ghosh","year":"2021","journal-title":"Medical Journal Armed Forces India"},{"key":"10.3233\/JIFS-224086_ref23","first-page":"38","article-title":"Comparative analysis of Machine learning-basedclassification models using sentiment classification of tweetsrelated to covid-19 pandemic","volume":"51","author":"Gulati","year":"2022","journal-title":"Materials Today: Proceedings"},{"key":"10.3233\/JIFS-224086_ref24","doi-asserted-by":"crossref","first-page":"992","DOI":"10.1109\/TCSS.2020.3042446","article-title":"Sentiment analysis oflockdown in india during covid-19: A case study on twitter","volume":"8","author":"Gupta","year":"2020","journal-title":"IEEE Transactions on Computational Social Systems"},{"key":"10.3233\/JIFS-224086_ref25","doi-asserted-by":"crossref","first-page":"110708","DOI":"10.1016\/j.chaos.2021.110708","article-title":"An emotion care model using multimodal textual analysison covid-19","volume":"144","author":"Gupta","year":"2021","journal-title":"Chaos, Solitons & Fractals"},{"key":"10.3233\/JIFS-224086_ref27","doi-asserted-by":"crossref","first-page":"181074","DOI":"10.1109\/ACCESS.2020.3027350","article-title":"Cross-culturalpolarity and emotion detection using sentiment analysis and deeplearning on covid-19 related tweets","volume":"8","author":"Imran","year":"2020","journal-title":"IEEE Access"},{"key":"10.3233\/JIFS-224086_ref29","doi-asserted-by":"crossref","first-page":"2733","DOI":"10.1109\/JBHI.2020.3001216","article-title":"Deep sentiment classification and topic discovery on novel coronavirus or covid-19 online discussions: Nlp using lstm recurrent neural network approach","volume":"24","author":"Jelodar","year":"2020","journal-title":"IEEE Journal of Biomedical and Health Informatics"},{"key":"10.3233\/JIFS-224086_ref32","doi-asserted-by":"crossref","unstructured":"Krkova V. , Manolopoulos Y. , Hammer B. , Iliadis L. and Maglogiannis I. , Artificial Neural Networks and Machine learning\u2013ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4\u20137, 2018, Proceedings, Part III. volume 11141. Springer, (2018).","DOI":"10.1007\/978-3-030-01424-7"},{"key":"10.3233\/JIFS-224086_ref34","first-page":"1","article-title":"Spatiotemporal sentiment variation analysis of geotaggedcovid-19 tweets from india using a hybrid deep learning model","volume":"12","author":"Kumar","year":"2022","journal-title":"Scientific Reports"},{"key":"10.3233\/JIFS-224086_ref35","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1016\/j.dsx.2021.03.021","article-title":"Whatindians think of the covid-19 vaccine: A qualitative studycomprising focus group discussions and thematic analysis","volume":"15","author":"Kumari","year":"2021","journal-title":"Diabetes & Metabolic Syndrome: Clinical Research & Reviews"},{"key":"10.3233\/JIFS-224086_ref36","doi-asserted-by":"crossref","first-page":"2790","DOI":"10.1007\/s10489-020-02029-z","article-title":"Design and analysis of a large-scale covid-19 tweets dataset","volume":"51","author":"Lamsal","year":"2021","journal-title":"Applied Intelligence"},{"key":"10.3233\/JIFS-224086_ref41","doi-asserted-by":"crossref","first-page":"1145","DOI":"10.1007\/s10796-021-10107-x","article-title":"A hybrid approach of and lexicons to sentiment analysis: enhancedin sights from twitter data of natural disasters","volume":"23","author":"Mendon","year":"2021","journal-title":"Information Systems Frontiers"},{"key":"10.3233\/JIFS-224086_ref44","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s13278-022-00885-w","article-title":"Public reactions towards covid-19 vaccination through twitter before and after secondwave in india","volume":"12","author":"Mishra","year":"2022","journal-title":"Social Network Analysis and Mining"},{"key":"10.3233\/JIFS-224086_ref45","doi-asserted-by":"crossref","first-page":"731","DOI":"10.1007\/s41027-021-00324-y","article-title":"Impact of covid 19 on indian migrant workers: decoding twitter data by text mining","volume":"64","author":"Misra","year":"2021","journal-title":"The Indian Journal of Labour Economics"},{"key":"10.3233\/JIFS-224086_ref46","first-page":"771","article-title":"A comprehensive analysis of convolutional neural network models","volume":"29","author":"Patel","year":"2020","journal-title":"International Journal of Advanced Science and Technology"},{"key":"10.3233\/JIFS-224086_ref47","unstructured":"Patil T.R. , Msss performance analysis of naive bayes and j48classification algorithm for data classification, Intl Journalof Computer Science and Applications 6 (2013)."},{"key":"10.3233\/JIFS-224086_ref48","doi-asserted-by":"crossref","first-page":"102118","DOI":"10.1016\/j.ajp.2020.102118","article-title":"Keeping the countrypositive during the covid 19 pandemic: Evidence from india","volume":"51","author":"Prabhu","year":"2020","journal-title":"Asian Journal of Psychiatry"},{"key":"10.3233\/JIFS-224086_ref49","doi-asserted-by":"crossref","first-page":"595","DOI":"10.1016\/j.dsx.2021.02.031","article-title":"Analyzing the attitude ofindian citizens towards covid-19 vaccine\u2013a text analyticsstudy","volume":"15","author":"Praveen","year":"2021","journal-title":"Diabetes & Metabolic Syndrome: Clinical Research & Reviews"},{"key":"10.3233\/JIFS-224086_ref51","doi-asserted-by":"crossref","first-page":"e2019EA000993","DOI":"10.1029\/2019EA000993","article-title":"Dictionary-based automatedinformation extraction from geological documents using a deeplearning algorithm","volume":"7","author":"Qiu","year":"2020","journal-title":"Earth and Space Science"},{"key":"10.3233\/JIFS-224086_ref53","doi-asserted-by":"crossref","first-page":"e0245909","DOI":"10.1371\/journal.pone.0245909","article-title":"A performance comparison of supervised Machine learning models for covid-19 tweets sentiment analysis","volume":"16","author":"Rustam","year":"2021","journal-title":"Plos One"},{"key":"10.3233\/JIFS-224086_ref54","doi-asserted-by":"crossref","first-page":"85721","DOI":"10.1109\/ACCESS.2021.3088838","article-title":"Determining the efficiency of drugs under special conditions from users\u2019 reviews on healthcare web forums","volume":"9","author":"Saad","year":"2021","journal-title":"IEEE Access"},{"key":"10.3233\/JIFS-224086_ref55","first-page":"1744","article-title":"Improving collaborative filtering using lexicon-based sentiment analysis","volume":"12","author":"Sallam","year":"2022","journal-title":"International Journal of Electrical and Computer Engineering"},{"key":"10.3233\/JIFS-224086_ref56","doi-asserted-by":"crossref","first-page":"314","DOI":"10.3390\/info11060314","article-title":"Covid-19 public sentiment insights and Machine learning for tweets classification","volume":"11","author":"Samuel","year":"2020","journal-title":"Information"},{"key":"10.3233\/JIFS-224086_ref57","doi-asserted-by":"crossref","first-page":"3709","DOI":"10.3390\/app12083709","article-title":"A deep learning approach for sentiment analysis of covid-19 reviews","volume":"12","author":"Singh","year":"2022","journal-title":"Applied Sciences"},{"key":"10.3233\/JIFS-224086_ref58","doi-asserted-by":"crossref","first-page":"1773","DOI":"10.4103\/ijo.IJO_324_22","article-title":"Twitter sentiment analysis for covid-19 associated mucormycosis","volume":"70","author":"Singh","year":"2022","journal-title":"Indian Journal of Ophthalmology"},{"key":"10.3233\/JIFS-224086_ref59","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s13278-021-00737-z","article-title":"Sentiment analysis on the impact of coronavirus in social life using the bert model","volume":"11","author":"Singh","year":"2021","journal-title":"Social Network Analysis and Mining"},{"key":"10.3233\/JIFS-224086_ref60","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1016\/j.patrec.2022.04.027","article-title":"Twitter sentiment analysis using ensemble based deep learning model towards covid-19 in india and european countries","volume":"158","author":"Sunitha","year":"2022","journal-title":"Pattern Recognition Letters"},{"key":"10.3233\/JIFS-224086_ref61","doi-asserted-by":"crossref","first-page":"102172","DOI":"10.1016\/j.dsx.2021.06.009","article-title":"Indian citizen\u2019s perspectiveabout side effects of covid-19 vaccine\u2013a Machine learningstudy","volume":"15","author":"Sv","year":"2021","journal-title":"Diabetes & Metabolic Syndrome: Clinical Research & Reviews"},{"key":"10.3233\/JIFS-224086_ref64","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1007\/s44212-022-00015-z","article-title":"Traffic flow prediction using bi-directional gated recurrent unit method","volume":"1","author":"Wang","year":"2022","journal-title":"Urban Informatics"},{"key":"10.3233\/JIFS-224086_ref65","doi-asserted-by":"crossref","first-page":"138162","DOI":"10.1109\/ACCESS.2020.3012595","article-title":"Covid-19 sensing: negativesentiment analysis on social media in china via bert model","volume":"8","author":"Wang","year":"2020","journal-title":"IEEE Access"},{"key":"10.3233\/JIFS-224086_ref67","doi-asserted-by":"crossref","first-page":"86","DOI":"10.2478\/dim-2020-0023","article-title":"Exploring public response to covid-19 on weibo with lda topic modeling and sentiment analysis","volume":"5","author":"Xie","year":"2021","journal-title":"Data and Information Management"},{"key":"10.3233\/JIFS-224086_ref68","doi-asserted-by":"crossref","first-page":"296","DOI":"10.3390\/axioms11060296","article-title":"Public opinion spread andguidance strategy under covid-19: A sis model analysis","volume":"11","author":"You","year":"2022","journal-title":"Axioms"},{"key":"10.3233\/JIFS-224086_ref69","doi-asserted-by":"crossref","unstructured":"Zhu X. , Zhang M. , Hong Y. and He R. , Natural Language Processing and Chinese Computing: 9th CCF International Conference, NLPCC 2020, Zhengzhou, China, October 14\u201318, 2020, Proceedings, Part I. volume 12430. Springer Nature, (2020).","DOI":"10.1007\/978-3-030-60450-9"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/JIFS-224086","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:45:45Z","timestamp":1777455945000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/JIFS-224086"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,2]]},"references-count":46,"journal-issue":{"issue":"1"},"URL":"https:\/\/doi.org\/10.3233\/jifs-224086","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,2]]}}}