{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T18:47:00Z","timestamp":1772045220494,"version":"3.50.1"},"reference-count":70,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2023,8,17]],"date-time":"2023-08-17T00:00:00Z","timestamp":1692230400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,8,17]],"date-time":"2023-08-17T00:00:00Z","timestamp":1692230400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Syst Assur Eng Manag"],"published-print":{"date-parts":[[2024,5]]},"DOI":"10.1007\/s13198-023-02082-0","type":"journal-article","created":{"date-parts":[[2023,8,17]],"date-time":"2023-08-17T10:02:11Z","timestamp":1692266531000},"page":"1756-1776","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Sentiment analysis and topic modeling of COVID-19 tweets of India"],"prefix":"10.1007","volume":"15","author":[{"given":"Manju","family":"Bhardwaj","sequence":"first","affiliation":[]},{"given":"Priya","family":"Mishra","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8535-1808","authenticated-orcid":false,"given":"Shikha","family":"Badhani","sequence":"additional","affiliation":[]},{"given":"Sunil K.","family":"Muttoo","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,8,17]]},"reference":[{"issue":"1","key":"2082_CR1","doi-asserted-by":"publisher","first-page":"626","DOI":"10.14569\/IJACSA.2021.0120172","volume":"12","author":"M Abdulaziz","year":"2021","unstructured":"Abdulaziz M, Alotaibi A, Alsolamy M, Alabbas A (2021) Topic based sentiment analysis for COVID-19 tweets. Int J Adv Comput Sci Appl 12(1):626\u2013636. https:\/\/doi.org\/10.14569\/IJACSA.2021.0120172","journal-title":"Int J Adv Comput Sci Appl"},{"key":"2082_CR2","unstructured":"Agarwal A, Xie B, Vovsha I, Rambow O, Passonneau R (2011) Sentiment analysis of twitter data. In: Proceedings of the workshop on language in social media (LSM 2011). Association for Computational Linguistics, Portland, Oregon, pp 30\u201338"},{"key":"2082_CR3","doi-asserted-by":"crossref","unstructured":"Agarwal B, Mittal N, Agarwal B, Mittal N (2016) Machine learning approach for sentiment analysis. In: Prominent feature extraction for sentiment analysis. Springer, pp 21\u201345","DOI":"10.1007\/978-3-319-25343-5_3"},{"issue":"7","key":"2082_CR4","doi-asserted-by":"publisher","first-page":"2169","DOI":"10.3837\/tiis.2022.07.003","volume":"16","author":"A Alamoodi","year":"2022","unstructured":"Alamoodi A, Baker MR, Albahri O, Zaidan B, Zaidan A, Wong W-K et al (2022) Public sentiment analysis and topic modeling regarding Covid-19\u2019s three waves of total lockdown: a case study on movement control order in malaysia. KSII Trans Internet Inf Syst 16(7):2169\u20132190. https:\/\/doi.org\/10.3837\/tiis.2022.07.003","journal-title":"KSII Trans Internet Inf Syst"},{"issue":"4","key":"2082_CR5","doi-asserted-by":"publisher","first-page":"479","DOI":"10.1504\/IJEG.2022.129304","volume":"14","author":"RM Aliguliyev","year":"2022","unstructured":"Aliguliyev RM, Iskandarli GY (2022) Measuring citizen satisfaction with e-government services by using sentiment analysis technology. Int Electron Govern 14(4):479\u2013489. https:\/\/doi.org\/10.1504\/IJEG.2022.129304","journal-title":"Int Electron Govern"},{"key":"2082_CR6","doi-asserted-by":"publisher","DOI":"10.1007\/s13369-021-05810-5","author":"JA Alzubi","year":"2021","unstructured":"Alzubi JA, Jain R, Singh A, Parwekar P, Gupta M (2021) COBERT: COVID-19 question answering system using BERT. Arab J Sci Eng. https:\/\/doi.org\/10.1007\/s13369-021-05810-5","journal-title":"Arab J Sci Eng"},{"key":"2082_CR7","doi-asserted-by":"publisher","first-page":"39313","DOI":"10.1109\/ACCESS.2022.3165621","volume":"10","author":"N Aslam","year":"2022","unstructured":"Aslam N, Rustam F, Lee E, Washington PB, Ashraf I (2022) Sentiment analysis and emotion detection on cryptocurrency related Tweets using ensemble LSTM\u2013GRU model. IEEE Access 10:39313\u201339324. https:\/\/doi.org\/10.1109\/ACCESS.2022.3165621","journal-title":"IEEE Access"},{"key":"2082_CR8","doi-asserted-by":"crossref","unstructured":"Bayhaqy A., Sfenrianto S, Nainggolan K, Kaburuan ER (2018) Sentiment analysis about e-commerce from tweets using decision tree, k-nearest neighbor, and Na\u00efve Bayes. In: 2018 international conference on Orange Technologies (ICOT), pp 1\u20136","DOI":"10.1109\/ICOT.2018.8705796"},{"issue":"1","key":"2082_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s13278-022-01000-9","volume":"13","author":"F Benrouba","year":"2023","unstructured":"Benrouba F, Boudour R (2023) Emotional sentiment analysis of social media content for mental health safety. Soc Netw Anal Min 13(1):1\u20138. https:\/\/doi.org\/10.1007\/s13278-022-01000-9","journal-title":"Soc Netw Anal Min"},{"key":"2082_CR10","volume-title":"Natural language processing with Python: analyzing text with the natural language toolkit","author":"S Bird","year":"2009","unstructured":"Bird S, Klein E, Loper E (2009) Natural language processing with Python: analyzing text with the natural language toolkit. O\u2019Reilly Media Inc, Sebastopol"},{"issue":"Jan","key":"2082_CR11","first-page":"993","volume":"3","author":"DM Blei","year":"2003","unstructured":"Blei DM, Ng AY, Jordan MI (2003) Latent Dirichlet allocation. J Mach Learn Res 3(Jan):993\u20131022","journal-title":"J Mach Learn Res"},{"issue":"10227","key":"2082_CR12","doi-asserted-by":"publisher","first-page":"912","DOI":"10.1016\/S0140-6736(20)30460-8","volume":"395","author":"SK Brooks","year":"2020","unstructured":"Brooks SK, Webster RK, Smith LE, Woodland L, Wessely S, Greenberg N, Rubin GJ (2020) The psychological impact of quarantine and how to reduce it: rapid review of the evidence. The Lancet 395(10227):912\u2013920. https:\/\/doi.org\/10.1016\/S0140-6736(20)30460-8","journal-title":"The Lancet"},{"issue":"12","key":"2082_CR13","doi-asserted-by":"publisher","first-page":"2928","DOI":"10.1109\/TKDE.2014.2313872","volume":"26","author":"X Cheng","year":"2014","unstructured":"Cheng X, Yan X, Lan Y, Guo J (2014) BTM: topic modeling over short texts. IEEE Trans Knowl Data Eng 26(12):2928\u20132941. https:\/\/doi.org\/10.1109\/TKDE.2014.2313872","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"2082_CR14","doi-asserted-by":"publisher","DOI":"10.1007\/978-81-322-3972-7","volume-title":"Fundamentals of artificial intelligence","author":"K Chowdhary","year":"2020","unstructured":"Chowdhary K (2020) Fundamentals of artificial intelligence. Springer, Berlin"},{"key":"2082_CR15","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1016\/j.cognition.2014.11.006","volume":"135","author":"U Cohen Priva","year":"2015","unstructured":"Cohen Priva U, Austerweil JL (2015) Analyzing the history of cognition using topic models. Cognition 135:4\u20139. https:\/\/doi.org\/10.1016\/j.cognition.2014.11.006","journal-title":"Cognition"},{"issue":"1","key":"2082_CR16","doi-asserted-by":"publisher","first-page":"157","DOI":"10.23750\/abm.v91i1.9397","volume":"91","author":"D Cucinotta","year":"2020","unstructured":"Cucinotta D, Vanelli M (2020) WHO declares COVID-19 a pandemic. Acta bio medica: Atenei parmensis 91(1):157\u2013160. https:\/\/doi.org\/10.23750\/abm.v91i1.9397","journal-title":"Acta bio medica: Atenei parmensis"},{"key":"2082_CR17","doi-asserted-by":"publisher","DOI":"10.5815\/ijieeb.2016.04.07","author":"L Dey","year":"2016","unstructured":"Dey L, Chakraborty S, Biswas A, Bose B, Tiwari S (2016) Sentiment analysis of review datasets using Naive Bayes and k-NN classifier. Int J Inf Eng Electron Bus. https:\/\/doi.org\/10.5815\/ijieeb.2016.04.07","journal-title":"Int J Inf Eng Electron Bus"},{"key":"2082_CR18","unstructured":"Dhawan B (2021) Twitter says it saw 600% increase in daily average tweets around COVID-19 during India\u2019s second wave of coronavirus. [June 1, 2023] https:\/\/www.financialexpress.com\/life\/technology-twitter-says-it-saw-600-increase-in-daily-average-tweets-around-covid-19-during-indias-second-wave-of-coronavirus-2281448\/"},{"issue":"4","key":"2082_CR19","doi-asserted-by":"publisher","first-page":"855","DOI":"10.1111\/jcc4.12078","volume":"19","author":"NB Ellison","year":"2014","unstructured":"Ellison NB, Vitak J, Gray R, Lampe C (2014) Cultivating social resources on social network sites: Facebook relationship maintenance behaviors and their role in social capital processes. J Comput Mediat Commun 19(4):855\u2013870. https:\/\/doi.org\/10.1111\/jcc4.12078","journal-title":"J Comput Mediat Commun"},{"key":"2082_CR20","doi-asserted-by":"crossref","unstructured":"Fitri VA, Andreswari R, Hasibuan MA (2019) Sentiment analysis of social media Twitter with case of anti-LGBT campaign in Indonesia using Na\u00efve Bayes, decision tree, and random forest algorithm. In: The fifth information systems international conference, 23\u201324 July 2019, Surabaya, Indonesia, vol 161. Elsevier, pp 765\u2013772","DOI":"10.1016\/j.procs.2019.11.181"},{"key":"2082_CR21","doi-asserted-by":"crossref","unstructured":"Gautam G, Yadav D (2014) Sentiment analysis of twitter data using machine learning approaches and semantic analysis. In: 2014 seventh international conference on contemporary computing (IC3). IEEE, pp 437\u2013442","DOI":"10.1109\/IC3.2014.6897213"},{"issue":"7825","key":"2082_CR22","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1038\/s41586-020-2649-2","volume":"585","author":"CR Harris","year":"2020","unstructured":"Harris CR, Millman KJ, van der Walt SJ, Gommers R, Virtanen P, Cournapeau D et al (2020) Array programming with NumPy. Nature 585(7825):357\u2013362. https:\/\/doi.org\/10.1038\/s41586-020-2649-2","journal-title":"Nature"},{"issue":"1","key":"2082_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/mca23010011","volume":"23","author":"A Hasan","year":"2018","unstructured":"Hasan A, Moin S, Karim A, Shamshirband S (2018) Machine learning-based sentiment analysis for twitter accounts. Math Comput Appl 23(1):1\u201315. https:\/\/doi.org\/10.3390\/mca23010011","journal-title":"Math Comput Appl"},{"key":"2082_CR24","unstructured":"Hydrator (2020) Documenting the now. [January 31, 2023] https:\/\/github.com\/docnow\/hydrator"},{"key":"2082_CR25","doi-asserted-by":"publisher","first-page":"15169","DOI":"10.1007\/s11042-018-6894-4","volume":"78","author":"H Jelodar","year":"2019","unstructured":"Jelodar H, Wang Y, Yuan C, Feng X, Jiang X, Li Y, Zhao L (2019) Latent Dirichlet Allocation (LDA) and topic modeling: models, applications, a survey. Multimed Tools Appl 78:15169\u201315211. https:\/\/doi.org\/10.1007\/s11042-018-6894-4","journal-title":"Multimed Tools Appl"},{"key":"2082_CR26","unstructured":"Johari A (2020) India\u2019s focus on coronavirus leaves TB and HIV patients adrift. [June 1, 2023] https:\/\/scroll.in\/article\/958400\/invisible-crisis-tb-and-hiv-patients-left-adrift-in-indias-focus-on-coronavirus"},{"key":"2082_CR27","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1016\/j.dss.2013.09.004","volume":"57","author":"FH Khan","year":"2014","unstructured":"Khan FH, Bashir S, Qamar U (2014) TOM: Twitter opinion mining framework using hybrid classification scheme. Decis Support Syst 57:245\u2013257. https:\/\/doi.org\/10.1016\/j.dss.2013.09.004","journal-title":"Decis Support Syst"},{"key":"2082_CR28","doi-asserted-by":"publisher","DOI":"10.1007\/s12652-020-01839-w","author":"W Khan","year":"2020","unstructured":"Khan W, Ghazanfar MA, Azam MA, Karami A, Alyoubi KH, Alfakeeh AS (2020) Stock market prediction using machine learning classifiers and social media, news. J Amb Intell Human Comput. https:\/\/doi.org\/10.1007\/s12652-020-01839-w","journal-title":"J Amb Intell Human Comput"},{"key":"2082_CR29","doi-asserted-by":"crossref","unstructured":"Khuc VN, Shivade C, Ramnath R, Ramanathan J (2012) Towards building large-scale distributed systems for twitter sentiment analysis. In: Proceedings of the 27th annual ACM symposium on applied computing, pp 459\u2013464","DOI":"10.1145\/2245276.2245364"},{"issue":"23","key":"2082_CR30","doi-asserted-by":"publisher","first-page":"2335","DOI":"10.1001\/jama.2021.9134","volume":"325","author":"BM Kuehn","year":"2021","unstructured":"Kuehn BM (2021) Despite improvements, COVID-19\u2019s health care disruptions persist. JAMA 325(23):2335. https:\/\/doi.org\/10.1001\/jama.2021.9134","journal-title":"JAMA"},{"key":"2082_CR31","unstructured":"Kulkarni T (2020) Cancer patients worried as hospitals focus on COVID-19. [June 1, 2023] https:\/\/www.thehindu.com\/news\/cities\/bangalore\/cancer-patients-worried-as-hospitals-focus-on-covid-19\/article31292061.ece\/amp\/"},{"issue":"5","key":"2082_CR32","doi-asserted-by":"publisher","first-page":"2790","DOI":"10.1007\/s10489-020-02029-z","volume":"51","author":"R Lamsal","year":"2021","unstructured":"Lamsal R (2021) Design and analysis of a large-scale COVID-19 tweets dataset. Appl Intell 51(5):2790\u20132804. https:\/\/doi.org\/10.1007\/s10489-020-02029-z","journal-title":"Appl Intell"},{"key":"2082_CR33","unstructured":"Lamsal R (2023) Coronavirus (COVID-19) Geo-tagged Tweets dataset. [March 21, 2023] https:\/\/ieee-dataport.org\/open-access\/coronavirus-covid-19-tweets-dataset"},{"key":"2082_CR34","first-page":"627","volume-title":"Handbook of natural language processing","author":"B Liu","year":"2010","unstructured":"Liu B et al (2010) Sentiment analysis and subjectivity. In: Indurkhya N, Damerau FJ (eds) Handbook of natural language processing. Chapman and Hall\/CRC, Boca Raton, pp 627\u2013666"},{"key":"2082_CR35","unstructured":"Loria S et\u00a0al (2020) TextBlob documentation, Release 0.16. [January 31, 2023] https:\/\/textblob.readthedocs.io\/en\/dev\/"},{"key":"2082_CR36","doi-asserted-by":"publisher","first-page":"102434","DOI":"10.1016\/j.technovation.2021.102434","volume":"114","author":"Q Lu","year":"2022","unstructured":"Lu Q, Chesbrough H (2022) Measuring open innovation practices through topic modelling: revisiting their impact on firm financial performance. Technovation 114:102434. https:\/\/doi.org\/10.1016\/j.technovation.2021.102434","journal-title":"Technovation"},{"key":"2082_CR37","doi-asserted-by":"publisher","first-page":"101760","DOI":"10.1016\/j.jairtraman.2019.101760","volume":"83","author":"FR Lucini","year":"2020","unstructured":"Lucini FR, Tonetto LM, Fogliatto FS, Anzanello MJ (2020) Text mining approach to explore dimensions of airline customer satisfaction using online customer reviews. J Air Transp Manag 83:101760. https:\/\/doi.org\/10.1016\/j.jairtraman.2019.101760","journal-title":"J Air Transp Manag"},{"issue":"8","key":"2082_CR38","doi-asserted-by":"publisher","first-page":"1317","DOI":"10.3390\/electronics9081317","volume":"9","author":"K Machov\u00e1","year":"2020","unstructured":"Machov\u00e1 K, Mikula M, Gao X, Mach M (2020) Lexicon-based sentiment analysis using the particle swarm optimization. Electronics 9(8):1317. https:\/\/doi.org\/10.3390\/electronics9081317","journal-title":"Electronics"},{"key":"2082_CR39","unstructured":"Mimno D, Wallach HM, Talley E, Leenders M, McCallum A (2011) Optimizing semantic coherence in topic models. In: Proceedings of the conference on empirical methods in natural language processing. Association for Computational Linguistics, USA, pp 262\u2013272"},{"issue":"10","key":"2082_CR40","doi-asserted-by":"publisher","first-page":"9166","DOI":"10.1016\/j.eswa.2012.02.057","volume":"39","author":"A Moreo","year":"2012","unstructured":"Moreo A, Romero M, Castro J, Zurita J (2012) Lexicon-based comments-oriented news sentiment analyzer system. Expert Syst Appl 39(10):9166\u20139180. https:\/\/doi.org\/10.1016\/j.eswa.2012.02.057","journal-title":"Expert Syst Appl"},{"issue":"18","key":"2082_CR41","doi-asserted-by":"publisher","first-page":"8438","DOI":"10.3390\/app11188438","volume":"11","author":"M Mujahid","year":"2021","unstructured":"Mujahid M, Lee E, Rustam F, Washington PB, Ullah S, Reshi AA, Ashraf I (2021) Sentiment analysis and topic modeling on tweets about online education during COVID-19. Appl Sci 11(18):8438. https:\/\/doi.org\/10.3390\/app11188438","journal-title":"Appl Sci"},{"key":"2082_CR42","unstructured":"News18 (2020) Day after honouring doctors with claps, many in India are evicting them fearing Covid-19. [June 1, 2023] https:\/\/www.news18.com\/news\/buzz\/day-after-honouring-doctors-with-claps-many-in-india-are-evicting-them-fearing-covid-19-2548937.html"},{"issue":"4","key":"2082_CR43","doi-asserted-by":"publisher","first-page":"125","DOI":"10.4018\/JOEUC.20210701.oa6","volume":"33","author":"C Ng","year":"2021","unstructured":"Ng C, Law KM, Ip AW (2021) Assessing public opinions of products through sentiment analysis: product satisfaction assessment by sentiment analysis. J Organ End User Comput (JOEUC) 33(4):125\u2013141. https:\/\/doi.org\/10.4018\/JOEUC.20210701.oa6","journal-title":"J Organ End User Comput (JOEUC)"},{"issue":"24","key":"2082_CR44","doi-asserted-by":"publisher","first-page":"9603","DOI":"10.1016\/j.eswa.2015.07.052","volume":"42","author":"TH Nguyen","year":"2015","unstructured":"Nguyen TH, Shirai K, Velcin J (2015) Sentiment analysis on social media for stock movement prediction. Expert Syst Appl 42(24):9603\u20139611. https:\/\/doi.org\/10.1016\/j.eswa.2015.07.052","journal-title":"Expert Syst Appl"},{"key":"2082_CR45","unstructured":"Palomino-Garibay A, Camacho-Gonzalez AT, Fierro-Villaneda RA, Hernandez-Farias I, Buscaldi D, Meza-Ruiz IV et\u00a0al (2015) A random forest approach for authorship profiling. In: Proceedings of CLEF"},{"key":"2082_CR46","doi-asserted-by":"crossref","unstructured":"Patel A, Meehan K (2021) Fake news detection on reddit utilising countvectorizer and term frequency-inverse document frequency with logistic regression, multinominalnb and support vector machine. In: 2021 32nd Irish signals and systems conference (ISSC), pp 1\u20136","DOI":"10.1109\/ISSC52156.2021.9467842"},{"key":"2082_CR47","first-page":"1","volume-title":"Computing attitude and affect in text: theory and applications","author":"L Polanyi","year":"2006","unstructured":"Polanyi L, Zaenen A (2006) Contextual valence shifters. In: Shanahan JG, Qu Y, Wiebe J (eds) Computing attitude and affect in text: theory and applications. Springer, Berlin, pp 1\u201310"},{"key":"2082_CR48","doi-asserted-by":"publisher","first-page":"S87","DOI":"10.1016\/j.diin.2018.04.023","volume":"26","author":"K Porter","year":"2018","unstructured":"Porter K (2018) Analyzing the darknetmarkets subreddit for evolutions of tools and trends using LDA topic modeling. Digit Investig 26:S87\u2013S97. https:\/\/doi.org\/10.1016\/j.diin.2018.04.023","journal-title":"Digit Investig"},{"issue":"3","key":"2082_CR49","doi-asserted-by":"publisher","first-page":"1","DOI":"10.4018\/JOEUC.294901","volume":"34","author":"F Qiao","year":"2022","unstructured":"Qiao F, Williams J (2022) Topic modelling and sentiment analysis of global warming tweets: evidence from big data analysis. J Organ End User Comput (JOEUC) 34(3):1\u201318. https:\/\/doi.org\/10.4018\/JOEUC.294901","journal-title":"J Organ End User Comput (JOEUC)"},{"key":"2082_CR50","unstructured":"\u0158eh\u016f\u0159ek R, Sojka P (2010) Software framework for topic modelling with large corpora. In: Proceedings of the LREC 2010 workshop on new challenges for NLP frameworks. ELRA, Valletta, Malta, pp 45\u201350"},{"key":"2082_CR51","doi-asserted-by":"crossref","unstructured":"R\u00f6der M, Both A, Hinneburg A (2015) Exploring the space of topic coherence measures. In: Proceedings of the eighth ACM international conference on web search and data mining. Association for Computing Machinery, New York, NY, USA, pp 399\u2013408","DOI":"10.1145\/2684822.2685324"},{"issue":"5","key":"2082_CR52","doi-asserted-by":"publisher","first-page":"1660","DOI":"10.18517\/ijaseit.7.4.2137","volume":"7","author":"B Saberi","year":"2017","unstructured":"Saberi B, Saad S (2017) Sentiment analysis or opinion mining: a review. Int J Adv Sci Eng Inf Technol 7(5):1660\u20131666. https:\/\/doi.org\/10.18517\/ijaseit.7.4.2137","journal-title":"Int J Adv Sci Eng Inf Technol"},{"issue":"1","key":"2082_CR53","doi-asserted-by":"publisher","first-page":"18273","DOI":"10.1038\/s41598-021-97421-1","volume":"11","author":"RS Sahi","year":"2021","unstructured":"Sahi RS, Schwyck ME, Parkinson C, Eisenberger NI (2021) Having more virtual interaction partners during COVID-19 physical distancing measures may benefit mental health. Sci Rep 11(1):18273. https:\/\/doi.org\/10.1038\/s41598-021-97421-1","journal-title":"Sci Rep"},{"key":"2082_CR54","doi-asserted-by":"crossref","unstructured":"Satya B, SJ MH, Rahardi M, Abdulloh FF (2022) Sentiment analysis of review sestyc using support vector machine, Naive Bayes, and logistic regression algorithm. In: 2022 5th international conference on information and communications technology (ICOIACT), pp 188\u2013193","DOI":"10.1109\/ICOIACT55506.2022.9972046"},{"issue":"2","key":"2082_CR55","doi-asserted-by":"publisher","first-page":"71","DOI":"10.3390\/info14020071","volume":"14","author":"S Schmidt","year":"2023","unstructured":"Schmidt S, Zorenb\u00f6hmer C, Arifi D, Resch B (2023) Polarity-based sentiment analysis of georeferenced Tweets related to the 2022 Twitter acquisition. Information 14(2):71. https:\/\/doi.org\/10.3390\/info14020071","journal-title":"Information"},{"key":"2082_CR56","doi-asserted-by":"crossref","unstructured":"Sharma A, Dey S (2012a) A comparative study of feature selection and machine learning techniques for sentiment analysis. In: Proceedings of the 2012 ACM research in applied computation symposium, pp 1\u20137","DOI":"10.1145\/2401603.2401605"},{"key":"2082_CR57","first-page":"15","volume":"3","author":"A Sharma","year":"2012","unstructured":"Sharma A, Dey S (2012) Performance investigation of feature selection methods and sentiment lexicons for sentiment analysis. IJCA special issue on advanced computing and communication technologies for HPC applications 3:15\u201320","journal-title":"IJCA special issue on advanced computing and communication technologies for HPC applications"},{"key":"2082_CR58","doi-asserted-by":"crossref","unstructured":"Sievert C, Shirley K (2014) LDAvis: a method for visualizing and interpreting topics. In: Proceedings of the workshop on interactive language learning, visualization, and interfaces. Association for Computational Linguistics, Baltimore, Maryland, USA, pp 63\u201370","DOI":"10.3115\/v1\/W14-3110"},{"issue":"1","key":"2082_CR59","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1007\/s13278-021-00737-z","volume":"11","author":"M Singh","year":"2021","unstructured":"Singh M, Jakhar AK, Pandey S (2021) Sentiment analysis on the impact of coronavirus in social life using the Bert model. Soc Netw Anal Min 11(1):33. https:\/\/doi.org\/10.1007\/s13278-021-00737-z","journal-title":"Soc Netw Anal Min"},{"key":"2082_CR60","unstructured":"Slater J, Masih N (2020) As pandemic intensifies, many in India die due to shortage of hospital beds. [June 1, 2023] https:\/\/www.seattletimes.com\/nation-world\/as-pandemic-intensifies-many-in-india-die-due-to-shortage-of-hospital-beds\/"},{"issue":"2","key":"2082_CR61","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1162\/coli_a_00049","volume":"37","author":"M Taboada","year":"2011","unstructured":"Taboada M, Brooke J, Tofiloski M, Voll K, Stede M (2011) Lexicon-based methods for sentiment analysis. Comput Linguist 37(2):267\u2013307. https:\/\/doi.org\/10.1162\/coli_a_00049","journal-title":"Comput Linguist"},{"key":"2082_CR62","doi-asserted-by":"crossref","unstructured":"Turney PD (2002) Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In: Proceedings of the 40th annual meeting on Association for Computational Linguistics. Association for Computational Linguistics, pp 417\u2013424","DOI":"10.3115\/1073083.1073153"},{"issue":"4","key":"2082_CR63","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1016\/j.icte.2020.07.003","volume":"6","author":"MA Ullah","year":"2020","unstructured":"Ullah MA, Marium SM, Begum SA, Dipa NS (2020) An algorithm and method for sentiment analysis using the text and emoticon. ICT Express 6(4):357\u2013360. https:\/\/doi.org\/10.1016\/j.icte.2020.07.003","journal-title":"ICT Express"},{"issue":"6","key":"2082_CR64","doi-asserted-by":"publisher","first-page":"634","DOI":"10.1016\/j.bspc.2013.06.004","volume":"8","author":"J Wang","year":"2013","unstructured":"Wang J, Liu P, She MF, Nahavandi S, Kouzani A (2013) Bag-of-words representation for biomedical time series classification. Biomed Signal Process Control 8(6):634\u2013644. https:\/\/doi.org\/10.1016\/j.bspc.2013.06.004","journal-title":"Biomed Signal Process Control"},{"key":"2082_CR65","doi-asserted-by":"crossref","unstructured":"Wang X, McCallum A (2006) Topics over time: a non-Markov continuous-time model of topical trends. In: Proceedings of the 12th ACM SIGKDD international conference on knowledge discovery and data mining. Association for Computing Machinery, pp 424\u2013433","DOI":"10.1145\/1150402.1150450"},{"key":"2082_CR66","doi-asserted-by":"crossref","unstructured":"Weng J, Lim EP, Jiang J, He Q (2010) Twitterrank: finding topic-sensitive influential twitterers. In: Proceedings of the third ACM international conference on web search and data mining. Association for Computing Machinery, pp 261\u2013270","DOI":"10.1145\/1718487.1718520"},{"key":"2082_CR67","unstructured":"Worldometer (2023) Coronavirus cases in India. [January 31, 2023] https:\/\/www.worldometers.info\/coronavirus\/country\/india\/"},{"key":"2082_CR68","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1016\/j.cie.2019.06.010","volume":"135","author":"H Xiong","year":"2019","unstructured":"Xiong H, Cheng Y, Zhao W, Liu J (2019) Analyzing scientific research topics in manufacturing field using a topic model. Comput Ind Eng 135:333\u2013347. https:\/\/doi.org\/10.1016\/j.cie.2019.06.010","journal-title":"Comput Ind Eng"},{"issue":"3","key":"2082_CR69","doi-asserted-by":"publisher","first-page":"1067","DOI":"10.1007\/s11280-022-01029-y","volume":"25","author":"H Yin","year":"2022","unstructured":"Yin H, Song X, Yang S, Li J (2022) Sentiment analysis and topic modeling for COVID-19 vaccine discussions. World Wide Web 25(3):1067\u20131083. https:\/\/doi.org\/10.1007\/s11280-022-01029-y","journal-title":"World Wide Web"},{"issue":"10","key":"2082_CR70","doi-asserted-by":"publisher","first-page":"1417","DOI":"10.1016\/S1874-1029(09)60057-4","volume":"36","author":"YY Zhao","year":"2010","unstructured":"Zhao YY, Qin B, Liu T (2010) Integrating intra- and inter-document evidences for improving sentence sentiment classification. Acta Autom Sin 36(10):1417\u20131425. https:\/\/doi.org\/10.1016\/S1874-1029(09)60057-4","journal-title":"Acta Autom Sin"}],"container-title":["International Journal of System Assurance Engineering and Management"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13198-023-02082-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13198-023-02082-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13198-023-02082-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,19]],"date-time":"2024-07-19T14:49:50Z","timestamp":1721400590000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13198-023-02082-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,17]]},"references-count":70,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2024,5]]}},"alternative-id":["2082"],"URL":"https:\/\/doi.org\/10.1007\/s13198-023-02082-0","relation":{},"ISSN":["0975-6809","0976-4348"],"issn-type":[{"value":"0975-6809","type":"print"},{"value":"0976-4348","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,17]]},"assertion":[{"value":"26 April 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 July 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 July 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 August 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no conflicts of interest to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}