{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T04:28:30Z","timestamp":1780892910280,"version":"3.54.1"},"reference-count":80,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,11,21]],"date-time":"2022-11-21T00:00:00Z","timestamp":1668988800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,11,21]],"date-time":"2022-11-21T00:00:00Z","timestamp":1668988800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"The Science Fund for the commercialization of scientific and technical activities, Kazakhstan","award":["0101-18-GC"],"award-info":[{"award-number":["0101-18-GC"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Big Data"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Social media services and analytics platforms are rapidly growing. A large number of various events happen mostly every day, and the role of social media monitoring tools is also increasing. Social networks are widely used for managing and promoting brands and different services. Thus, most popular social analytics platforms aim for business purposes while monitoring various social, economic, and political problems remains underrepresented and not covered by thorough research. Moreover, most of them focus on resource-rich languages such as the English language, whereas texts and comments in other low-resource languages, such as the Russian and Kazakh languages in social media, are not represented well enough. So, this work is devoted to developing and applying the information system called the OMSystem for analyzing users\u2019 opinions on news portals, blogs, and social networks in Kazakhstan. The system uses sentiment dictionaries of the Russian and Kazakh languages and machine learning algorithms to determine the sentiment of social media texts. The whole structure and functionalities of the system are also presented. The experimental part is devoted to building machine learning models for sentiment analysis on the Russian and Kazakh datasets. Then the performance of the models is evaluated with accuracy, precision, recall, and F1-score metrics. The models with the highest scores are selected for implementation in the OMSystem. Then the OMSystem\u2019s social analytics module is used to thoroughly analyze the healthcare, political and social aspects of the most relevant topics connected with the vaccination against the coronavirus disease. The analysis allowed us to discover the public social mood in the cities of Almaty and Nur-Sultan and other large regional cities of Kazakhstan. The system\u2019s study included two extensive periods: 10-01-2021 to 30-05-2021 and 01-07-2021 to 12-08-2021. In the obtained results, people\u2019s moods and attitudes to the Government\u2019s policies and actions were studied by such social network indicators as the level of topic discussion activity in society, the level of interest in the topic in society, and the mood level of society. These indicators calculated by the OMSystem allowed careful identification of alarming factors of the public (negative attitude to the government regulations, vaccination policies, trust in vaccination, etc.) and assessment of the social mood.<\/jats:p>","DOI":"10.1186\/s40537-022-00660-w","type":"journal-article","created":{"date-parts":[[2022,11,22]],"date-time":"2022-11-22T23:05:03Z","timestamp":1669158303000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["On the development of an information system for monitoring user opinion and its role for the public"],"prefix":"10.1186","volume":"9","author":[{"given":"Vladislav","family":"Karyukin","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Galimkair","family":"Mutanov","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhanl","family":"Mamykova","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gulnar","family":"Nassimova","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saule","family":"Torekul","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhanerke","family":"Sundetova","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matteo","family":"Negri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,11,21]]},"reference":[{"key":"660_CR1","unstructured":"Esteban OO. The rise of social media. Our world in data; 2019. https:\/\/ourworldindata.org\/rise-of-social-media."},{"key":"660_CR2","doi-asserted-by":"crossref","unstructured":"Chaffey D. Global social media statistics research summary 2022. Smart insights; 2022. https:\/\/www.smartinsights.com\/social-media-marketing\/social-media-strategy\/new-global-social-media-research\/.","DOI":"10.4324\/9781003009498-6"},{"issue":"1","key":"660_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ipm.2021.102762","volume":"59","author":"H Zhang","year":"2022","unstructured":"Zhang H, Zang Zh, Zhu H, Uddin MI, Amin MA. Big data-assisted social media analytics for business model for business decision making system competitive analysis. Inf Process Manag. 2022;59(1):1\u201312.","journal-title":"Inf Process Manag"},{"key":"660_CR4","doi-asserted-by":"publisher","unstructured":"Singh H, Yadav A, Bansal R, Mala S. Understanding brand authenticity sentiments using big data analytics. In: 11th international conference on cloud computing, data science & engineering (confluence). 2021. p. 304\u20138. https:\/\/doi.org\/10.1109\/Confluence51648.2021.9377046.","DOI":"10.1109\/Confluence51648.2021.9377046"},{"key":"660_CR5","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1016\/j.intmar.2021.05.001","volume":"56","author":"U Pamuksuz","year":"2021","unstructured":"Pamuksuz U, Yun JT, Humphreys A. A brand-new look at you: predicting brand personality in social media networks with machine learning. J Interact Mark. 2021;56:55\u201369. https:\/\/doi.org\/10.1016\/j.intmar.2021.05.001.","journal-title":"J Interact Mark"},{"key":"660_CR6","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1140\/epjds\/s13688-021-00305-7","volume":"10","author":"S Praet","year":"2021","unstructured":"Praet S, Van Aelst P, van Erkel P, Van der Veeken S, Martens D. Predictive modeling to study lifestyle politics with Facebook likes. EPJ Data Sci. 2021;10:50. https:\/\/doi.org\/10.1140\/epjds\/s13688-021-00305-7.","journal-title":"EPJ Data Sci"},{"key":"660_CR7","doi-asserted-by":"publisher","unstructured":"Chandra Sekhar Reddy N, Subhashini V, Rai D, Sriharsha, Vittal B, Ganesh S. Product rating estimation using machine learning. In: 6th international conference on communication and electronics systems (ICCES). 2021. p. 1366\u20139. https:\/\/doi.org\/10.1109\/ICCES51350.2021.9489208.","DOI":"10.1109\/ICCES51350.2021.9489208"},{"issue":"3","key":"660_CR8","first-page":"5399","volume":"70","author":"D Dangi","year":"2022","unstructured":"Dangi D, Bhagat A, Dixit DK. Emerging applications of artificial intelligence, machine learning and data science. Comput Mater Contin. 2022;70(3):5399\u2013419.","journal-title":"Comput Mater Contin"},{"key":"660_CR9","doi-asserted-by":"publisher","DOI":"10.1007\/s13278-022-00922-8","author":"D Karamouzas","year":"2022","unstructured":"Karamouzas D, Mademlis I, Pitas I. Public opinion monitoring through collective semantic analysis of tweets. Soc Netw Anal Min. 2022. https:\/\/doi.org\/10.1007\/s13278-022-00922-8.","journal-title":"Soc Netw Anal Min"},{"key":"660_CR10","doi-asserted-by":"publisher","DOI":"10.1007\/s13278-022-00913-9","author":"L Belcastro","year":"2022","unstructured":"Belcastro L, Branda F, Cantini R, et al. Analyzing voter behavior on social media during the 2020 US presidential election campaign. Soc Netw Anal Min. 2022. https:\/\/doi.org\/10.1007\/s13278-022-00913-9.","journal-title":"Soc Netw Anal Min"},{"key":"660_CR11","doi-asserted-by":"publisher","unstructured":"Negrete JCM, Iano Y, Negrete PDM, Vaz GC, de Oliveira GG. Sentiment and emotions analysis of tweets during the second round of 2021 ecuadorian presidential election. In: Proceedings of the 7th Brazilian technology symposium (BTSym\u201921). BTSym 2021. Smart innovation, systems and technologies, vol. 207. Cham: Springer; 2023. https:\/\/doi.org\/10.1007\/978-3-031-04435-9_24.","DOI":"10.1007\/978-3-031-04435-9_24"},{"key":"660_CR12","series-title":"Lecture notes on data engineering and communications technologies","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-90618-4_30","volume-title":"AI and IoT for sustainable development in emerging countries","author":"A Oussous","year":"2022","unstructured":"Oussous A, Boulouard Z, Zahra BF. Prediction and analysis of moroccan elections using sentiment analysis. In: AI and IoT for sustainable development in emerging countries, vol. 105. Lecture notes on data engineering and communications technologies. Cham: Springer; 2022. https:\/\/doi.org\/10.1007\/978-3-030-90618-4_30."},{"key":"660_CR13","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1186\/s40537-022-00620-4","volume":"9","author":"K Ali","year":"2022","unstructured":"Ali K, Hamilton M, Thevathayan C, et al. Big social data as a service (BSDaaS): a service composition framework for social media analysis. J Big Data. 2022;9:64. https:\/\/doi.org\/10.1186\/s40537-022-00620-4.","journal-title":"J Big Data"},{"issue":"3","key":"660_CR14","first-page":"4987","volume":"70","author":"MA Qureshi","year":"2022","unstructured":"Qureshi MA, Asif M, Hassan MF, Mustafa G, Ehsan MK, Ali A, Sajid U. A novel auto-annotation technique for aspect level sentiment analysis. Comput Mater Contin. 2022;70(3):4987\u20135004.","journal-title":"Comput Mater Contin"},{"key":"660_CR15","doi-asserted-by":"publisher","first-page":"308","DOI":"10.3390\/info13070308","volume":"13","author":"A Aldawod","year":"2022","unstructured":"Aldawod A, Alsakran R, Alrasheed H. Understanding entertainment trends during COVID-19 in Saudi Arabia. Information. 2022;13:308. https:\/\/doi.org\/10.3390\/info13070308.","journal-title":"Information"},{"issue":"1","key":"660_CR16","doi-asserted-by":"publisher","first-page":"69","DOI":"10.31449\/inf.v46i1.3483","volume":"46","author":"SK Akpatsa","year":"2022","unstructured":"Akpatsa SK, Lei H, Li X, KofiSetornyoObeng VH. Evaluating public sentiments of Covid-19 vaccine tweets using machine learning techniques. Informatica. 2022;46(1):69\u201375. https:\/\/doi.org\/10.31449\/inf.v46i1.3483.","journal-title":"Informatica"},{"key":"660_CR17","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1186\/s40537-022-00594-3","volume":"9","author":"S Thara","year":"2022","unstructured":"Thara S, Poornachandran P. Social media text analytics of Malayalam\u2013English code-mixed using deep learning. J Big Data. 2022;9:45. https:\/\/doi.org\/10.1186\/s40537-022-00594-3.","journal-title":"J Big Data"},{"key":"660_CR18","doi-asserted-by":"publisher","first-page":"11236","DOI":"10.1038\/s41598-022-14579-y","volume":"12","author":"M Pellert","year":"2022","unstructured":"Pellert M, Metzler H, Matzenberger M, et al. Validating daily social media macroscopes of emotions. Sci Rep. 2022;12:11236. https:\/\/doi.org\/10.1038\/s41598-022-14579-y.","journal-title":"Sci Rep"},{"key":"660_CR19","series-title":"Studies in computational intelligence","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1007\/978-3-319-30319-2_14","volume-title":"Sentiment analysis and ontology engineering","author":"F Benedetto","year":"2016","unstructured":"Benedetto F, Tedeschi A. Big data sentiment analysis for brand monitoring in social media streams by cloud computing. In: Pedrycz W, Chen SM, editors. Sentiment analysis and ontology engineering. Studies in computational intelligence. Cham: Springer; 2016. p. 341\u201377. https:\/\/doi.org\/10.1007\/978-3-319-30319-2_14."},{"key":"660_CR20","series-title":"Lecture notes in computer science","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1007\/978-3-319-70284-1_28","volume-title":"Internet science. INSCI 2017","author":"M Schinas","year":"2017","unstructured":"Schinas M, Papadopoulos S, Apostolidis L, Kompatsiaris Y, Mitkas PA, et al. Open-source monitoring, search and analytics over social media. In: Kompatsiaris I, et al., editors. Internet science. INSCI 2017. Lecture notes in computer science. Cham: Springer; 2017. p. 361\u20139. https:\/\/doi.org\/10.1007\/978-3-319-70284-1_28."},{"key":"660_CR21","doi-asserted-by":"publisher","first-page":"13207","DOI":"10.1038\/s41598-021-92337-2","volume":"11","author":"T Radicioni","year":"2021","unstructured":"Radicioni T, Saracco F, Pavan E, Squartini T. Analysing Twitter semantic networks: the case of 2018 Italian elections. Sci Rep. 2021;11:13207. https:\/\/doi.org\/10.1038\/s41598-021-92337-2.","journal-title":"Sci Rep"},{"key":"660_CR22","doi-asserted-by":"publisher","DOI":"10.1007\/s13278-021-00752-0","author":"S Bhatnagar","year":"2021","unstructured":"Bhatnagar S, Choubey N. Making sense of tweets using sentiment analysis on closely related topics. Soc Netw Anal Min. 2021. https:\/\/doi.org\/10.1007\/s13278-021-00752-0.","journal-title":"Soc Netw Anal Min"},{"key":"660_CR23","doi-asserted-by":"publisher","DOI":"10.1007\/s13278-021-00776-6","author":"P Nandwani","year":"2021","unstructured":"Nandwani P, Verma R. A review on sentiment analysis and emotion detection from text. Soc Netw Anal Min. 2021. https:\/\/doi.org\/10.1007\/s13278-021-00776-6.","journal-title":"Soc Netw Anal Min"},{"issue":"1","key":"660_CR24","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1016\/j.ijresmar.2018.09.009","volume":"36","author":"J Hartmann","year":"2021","unstructured":"Hartmann J, Huppertz J, Schamp C, Heitmann M. Comparing automated text classification methods. Int J Res Mark. 2021;36(1):20\u201338. https:\/\/doi.org\/10.1016\/j.ijresmar.2018.09.009.","journal-title":"Int J Res Mark"},{"key":"660_CR25","doi-asserted-by":"publisher","DOI":"10.14569\/IJACSA.2017.080603","author":"MR Huq","year":"2017","unstructured":"Huq MR, Ali A, Rahman A. Sentiment analysis on Twitter data using KNN and SVM. Int J Adv Comput Sci Appl. 2017. https:\/\/doi.org\/10.14569\/IJACSA.2017.080603.","journal-title":"Int J Adv Comput Sci Appl"},{"issue":"3","key":"660_CR26","doi-asserted-by":"publisher","first-page":"483","DOI":"10.3390\/electronics9030483","volume":"9","author":"NC Dang","year":"2020","unstructured":"Dang NC, Moreno-Garc\u00eda MN, De la Prieta F. Sentiment analysis based on deep learning: a comparative study. Electronics. 2020;9(3):483. https:\/\/doi.org\/10.3390\/electronics9030483.","journal-title":"Electronics"},{"key":"660_CR27","series-title":"Lecture notes in computer science","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1007\/978-3-030-61841-4_8","volume-title":"Disinformation in open online media. MISDOOM 2020","author":"D R\u00f6chert","year":"2020","unstructured":"R\u00f6chert D, Neubaum G, Stieglitz S. Identifying political sentiments on YouTube: a systematic comparison regarding the accuracy of recurrent neural network and machine learning models. In: van Duijn M, Preuss M, Spaiser V, Takes F, Verberne S, editors. Disinformation in open online media. MISDOOM 2020. Lecture notes in computer science. Cham: Springer; 2020. p. 107\u201321. https:\/\/doi.org\/10.1007\/978-3-030-61841-4_8."},{"key":"660_CR28","doi-asserted-by":"publisher","DOI":"10.1007\/s13278-020-00668-1","author":"AH Ombabi","year":"2020","unstructured":"Ombabi AH, Ouarda W, Alimi AM. Deep learning CNN\u2013LSTM framework for Arabic sentiment analysis using textual information shared in social networks. Soc Netw Anal Min. 2020. https:\/\/doi.org\/10.1007\/s13278-020-00668-1.","journal-title":"Soc Netw Anal Min"},{"key":"660_CR29","unstructured":"Znovarev A, Bilyi A. A comparison of machine learning methods of sentiment analysis based on Russian language Twitter data. In: 11th Majorov international conference on software engineering and computer systems, MICSECS, Saint Petersburg, Russian Federation. 2020. p. 1\u20137."},{"key":"660_CR30","doi-asserted-by":"crossref","unstructured":"Hamada MA, Sultanbek K, Alzhanov B, Tokbanov B. Sentimental text processing tool for Russian language based on machine learning algorithms. In: Proceedings of the 5th international conference on engineering and MIS, Astana, Kazakhstan. 2019. p. 1\u20136.","DOI":"10.1145\/3330431.3335204"},{"key":"660_CR31","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/23311916.2020.1856500","volume":"7","author":"U Tukeyev","year":"2022","unstructured":"Tukeyev U, Karibayeva A, Zhumanov Zh. Morphological segmentation method for Turkic language neural machine translation. Cogent Eng. 2022;7:1. https:\/\/doi.org\/10.1080\/23311916.2020.1856500.","journal-title":"Cogent Eng"},{"issue":"1","key":"660_CR32","doi-asserted-by":"publisher","first-page":"9","DOI":"10.3233\/WEB-190396","volume":"17","author":"B Yergesh","year":"2019","unstructured":"Yergesh B, Bekmanova G, Sharipbay A. Sentiment analysis of Kazakh text and their polarity. Web Intell. 2019;17(1):9\u201315.","journal-title":"Web Intell"},{"key":"660_CR33","doi-asserted-by":"crossref","unstructured":"Bekmanova G, Yelibayeva G, Aubakirova S, Dyussupova N, Sharipbay A, Niyazova N. Methods for analyzing polarity of the Kazakh texts related to the terrorist threats. In: 19th international conference on computational science and its applications, ICCSA, Saint Petersburg, Russian Federation. 2019. p. 717\u201330.","DOI":"10.1007\/978-3-030-24289-3_53"},{"issue":"21","key":"660_CR34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40649-021-00101-3","volume":"8","author":"H Alzahrani","year":"2021","unstructured":"Alzahrani H, Acharya S, Duverger P, Nguyen NP. Contextual polarity and influence mining in online social networks. Comput Soc Netw. 2021;8(21):1\u201327. https:\/\/doi.org\/10.1186\/s40649-021-00101-3.","journal-title":"Comput Soc Netw"},{"issue":"62","key":"660_CR35","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s13278-021-00770-y","volume":"11","author":"D Weber","year":"2021","unstructured":"Weber D, Nasim M, Mitchell L, Falzon L. Exploring the effect of streamed social media data variations on social network analysis. Soc Netw Anal Min. 2021;11(62):1\u201338. https:\/\/doi.org\/10.1007\/s13278-021-00770-y.","journal-title":"Soc Netw Anal Min"},{"key":"660_CR36","unstructured":"Sproutsocial. https:\/\/sproutsocial.com\/. Accessed 27 Nov 2021."},{"key":"660_CR37","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-021-00466-2","author":"K Chaudhary","year":"2021","unstructured":"Chaudhary K, Alam M, Al-Rakhami MS, Gumaei A. Machine learning-based mathematical modelling for prediction of social media consumer behavior using big data analytics. J Big Data. 2021. https:\/\/doi.org\/10.1186\/s40537-021-00466-2.","journal-title":"J Big Data"},{"key":"660_CR38","unstructured":"Hubspot. https:\/\/www.hubspot.com\/. Accessed 27 Nov 2021."},{"key":"660_CR39","unstructured":"Buzzsumo. https:\/\/buzzsumo.com\/. Accessed 27 Nov 2021."},{"key":"660_CR40","doi-asserted-by":"publisher","DOI":"10.1016\/j.elerap.2021.101068","volume":"48","author":"Q Deng","year":"2021","unstructured":"Deng Q, Hine MJ, Ji Sh, Wang Y. Understanding consumer engagement with brand posts on social media: the effects of post linguistic styles. Electron Commer Res Appl. 2021;48: 101068. https:\/\/doi.org\/10.1016\/j.elerap.2021.101068.","journal-title":"Electron Commer Res Appl"},{"key":"660_CR41","unstructured":"Hootsuite. https:\/\/www.hootsuite.com\/. Accessed 27 Nov 2021."},{"key":"660_CR42","unstructured":"Brandmention. https:\/\/brandmentions.com\/. Accessed 27 Nov 2021."},{"key":"660_CR43","doi-asserted-by":"publisher","unstructured":"Rahmatulloh A, Shofa RN, Darmawan I, Ardiansah. Sentiment analysis of Ojek online user satisfaction based on the Na\u00efve Bayes and net brand reputation method. In: 9th international conference on information and communication technology (ICoICT). 2021. p. 337\u201341. https:\/\/doi.org\/10.1109\/ICoICT52021.2021.9527466.","DOI":"10.1109\/ICoICT52021.2021.9527466"},{"key":"660_CR44","unstructured":"IQBuzz. https:\/\/iqbuzz.pro\/. Accessed 27 Nov 2021."},{"key":"660_CR45","series-title":"Smart innovation, systems and technologies","doi-asserted-by":"publisher","first-page":"216","DOI":"10.1007\/978-981-16-5792-4_22","volume-title":"Communication and smart technologies. ICOMTA 2021","author":"J Beltr\u00e1n","year":"2022","unstructured":"Beltr\u00e1n J, Jara-Reyes R, Faure A. The emotions of the outbreak. Topics, sentiments and politics on Twitter during Chilean October. In: Rocha \u00c1, Barredo D, L\u00f3pez-L\u00f3pez PC, Puentes-Rivera I, editors. Communication and smart technologies. ICOMTA 2021. Smart innovation, systems and technologies. Singapore: Springer; 2022. p. 216\u201326. https:\/\/doi.org\/10.1007\/978-981-16-5792-4_22."},{"key":"660_CR46","unstructured":"Snaplytics. https:\/\/thehub.io\/startups\/snaplytics. Accessed 27 Nov 2021."},{"key":"660_CR47","unstructured":"iMAS. https:\/\/imas.kz\/. Accessed 27 Nov 2021."},{"key":"660_CR48","doi-asserted-by":"crossref","unstructured":"Alem media monitoring. https:\/\/alem.kz\/en\/monitoring-smi\/. Accessed 27 Nov 2021.","DOI":"10.1017\/S1431927621009387"},{"key":"660_CR49","series-title":"Lecture notes in networks and systems","doi-asserted-by":"publisher","first-page":"981","DOI":"10.1007\/978-3-030-80119-9_65","volume-title":"Intelligent computing","author":"B Usero","year":"2022","unstructured":"Usero B, Hern\u00e1ndez V, Quintana C. Social media mining for business intelligence analytics: an application for movie box office forecasting. In: Arai K, editor. Intelligent computing. Lecture notes in networks and systems. Cham: Springer; 2022. p. 981\u201399. https:\/\/doi.org\/10.1007\/978-3-030-80119-9_65."},{"issue":"1","key":"660_CR50","first-page":"913","volume":"69","author":"G Mutanov","year":"2021","unstructured":"Mutanov G, Karyukin V, Mamykova Z. Multiclass sentiment analysis of social media data with machine learning algorithms. Comput Mater Contin. 2021;69(1):913\u201330.","journal-title":"Comput Mater Contin"},{"key":"660_CR51","doi-asserted-by":"publisher","unstructured":"Kadyrbek N, Sundetova Zh, Torekul S. Information monitoring system of social wellness opinions. In: IEEE 8th workshop on advances in information, electronic and electrical engineering (AIEEE). 2021. p. 1\u20134. https:\/\/doi.org\/10.1109\/AIEEE51419.2021.9435811.","DOI":"10.1109\/AIEEE51419.2021.9435811"},{"issue":"1","key":"660_CR52","first-page":"915","volume":"70","author":"Sh Mussiraliyeva","year":"2021","unstructured":"Mussiraliyeva Sh, Omarov B, Yoo P, Bolatbek M. Applying machine learning techniques for religious extremism detection on online user contents. Comput Mater Contin. 2021;70(1):915\u201334.","journal-title":"Comput Mater Contin"},{"key":"660_CR53","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-021-00523-w","author":"EAH Khalil","year":"2021","unstructured":"Khalil EAH, Houby EMFE, Mohamed HK. Deep learning for emotion analysis in Arabic tweets. J Big Data. 2021. https:\/\/doi.org\/10.1186\/s40537-021-00523-w.","journal-title":"J Big Data"},{"key":"660_CR54","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-021-00431-z","author":"D Domalewska","year":"2021","unstructured":"Domalewska D. An analysis of COVID-19 economic measures and attitudes: evidence from social media mining. J Big Data. 2021. https:\/\/doi.org\/10.1186\/s40537-021-00431-z.","journal-title":"J Big Data"},{"key":"660_CR55","doi-asserted-by":"publisher","DOI":"10.1007\/s13278-021-00732-4","author":"GR Ramya","year":"2021","unstructured":"Ramya GR, Bagavathi SP. An incremental learning temporal influence model for identifying topical influencers on Twitter dataset. Soc Netw Anal Min. 2021. https:\/\/doi.org\/10.1007\/s13278-021-00732-4.","journal-title":"Soc Netw Anal Min"},{"key":"660_CR56","doi-asserted-by":"publisher","unstructured":"Heidari M, Shamsinejad P. Producing an instagram dataset for persian language sentiment analysis using crowdsourcing method. In: 6th international conference on web research (ICWR). 2020. p. 284\u20137. https:\/\/doi.org\/10.1109\/ICWR49608.2020.9122270.","DOI":"10.1109\/ICWR49608.2020.9122270"},{"key":"660_CR57","doi-asserted-by":"publisher","first-page":"290","DOI":"10.1016\/j.future.2020.08.006","volume":"114","author":"D Camacho","year":"2021","unstructured":"Camacho D, Luz\u00f3n MV, Cambria E. New research methods & algorithms in social network analysis. Futur Gener Comput Syst. 2021;114:290\u20133. https:\/\/doi.org\/10.1016\/j.future.2020.08.006.","journal-title":"Futur Gener Comput Syst"},{"key":"660_CR58","doi-asserted-by":"publisher","unstructured":"Zarzour H, Al shboul B, Al-Ayyoub M, Jararweh Y. Sentiment analysis based on deep learning methods for explainable recommendations with reviews. In: 12th international conference on information and communication systems (ICICS). 2021. p. 452\u20136. https:\/\/doi.org\/10.1109\/ICICS52457.2021.9464601.","DOI":"10.1109\/ICICS52457.2021.9464601"},{"issue":"2","key":"660_CR59","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1109\/MIC.2020.3040516","volume":"25","author":"LHX Ng","year":"2021","unstructured":"Ng LHX, Loke JY. Analyzing public opinion and misinformation in a COVID-19 telegram group chat. IEEE Internet Comput. 2021;25(2):84\u201391. https:\/\/doi.org\/10.1109\/MIC.2020.3040516.","journal-title":"IEEE Internet Comput"},{"key":"660_CR60","doi-asserted-by":"publisher","first-page":"176","DOI":"10.14515\/monitoring.2020.4.1254","volume":"4","author":"DY Kulchitskaya","year":"2020","unstructured":"Kulchitskaya DY, Folts AO. Between politics and show business: public discourse on social media regarding ksenia sobchak, the only female candidate in the2018 Russian presidential election. Monitor Obshchestvennogo Mneniya Ekonomicheskie i Sotsial\u2019nye Peremeny. 2020;4:176\u201399. https:\/\/doi.org\/10.14515\/monitoring.2020.4.1254.","journal-title":"Monitor Obshchestvennogo Mneniya Ekonomicheskie i Sotsial'nye Peremeny"},{"key":"660_CR61","doi-asserted-by":"publisher","first-page":"7995","DOI":"10.1007\/s10489-021-02809-1","volume":"52","author":"J Chen","year":"2022","unstructured":"Chen J, Chen Y, He Y, et al. A classified feature representation three-way decision model for sentiment analysis. Appl Intell. 2022;52:7995\u20138007. https:\/\/doi.org\/10.1007\/s10489-021-02809-1.","journal-title":"Appl Intell"},{"issue":"3","key":"660_CR62","doi-asserted-by":"publisher","first-page":"151","DOI":"10.3390\/info13030151","volume":"13","author":"MC Buzea","year":"2022","unstructured":"Buzea MC, Stefan TM, Traian R. Automatic fake news detection for romanian online news. Information. 2022;13(3):151. https:\/\/doi.org\/10.3390\/info13030151.","journal-title":"Information"},{"issue":"2","key":"660_CR63","doi-asserted-by":"publisher","first-page":"58","DOI":"10.3390\/bdcc6020058","volume":"6","author":"Y Didi","year":"2022","unstructured":"Didi Y, Ahlam W, Ali W. COVID-19 tweets classification based on a hybrid word embedding method. Big Data Cogn Comput. 2022;6(2):58. https:\/\/doi.org\/10.3390\/bdcc6020058.","journal-title":"Big Data Cogn Comput"},{"key":"660_CR64","doi-asserted-by":"publisher","DOI":"10.11591\/ijai.v11.i3.pp1085-1093","author":"P Vigneshwaran","year":"2022","unstructured":"Vigneshwaran P, Prasath N, Sindhuja M, Islabudeen MM, Ragaventhiran J, Muthu KB. A comprehensive analysis of consumer decisions on Twitter dataset using machine learning algorithms. Int J Artif Intell. 2022. https:\/\/doi.org\/10.11591\/ijai.v11.i3.pp1085-1093.","journal-title":"Int J Artif Intell"},{"key":"660_CR65","doi-asserted-by":"publisher","DOI":"10.21817\/indjcse\/2022\/v13i3\/221303003","author":"F Hassan","year":"2022","unstructured":"Hassan F, El Hicham M, Hicham L, Ali Y. Sentiment analysis of Arabic comments using machine learning and deep learning models. Indian J Comput Sci Eng. 2022. https:\/\/doi.org\/10.21817\/indjcse\/2022\/v13i3\/221303003.","journal-title":"Indian J Comput Sci Eng"},{"key":"660_CR66","doi-asserted-by":"publisher","first-page":"5203","DOI":"10.1007\/s11227-021-04087-7","volume":"78","author":"PK Jain","year":"2022","unstructured":"Jain PK, Pamula R, Yekun EA. A multi-label ensemble predicting model to service recommendation from social media contents. J Supercomput. 2022;78:5203\u201320. https:\/\/doi.org\/10.1007\/s11227-021-04087-7.","journal-title":"J Supercomput"},{"key":"660_CR67","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1057\/s41310-021-00129-x","volume":"19","author":"GA Mousa","year":"2022","unstructured":"Mousa GA, Elamir EAH, Hussainey K. Using machine learning methods to predict financial performance: does disclosure tone matter? Int J Discl Gov. 2022;19:93\u2013112. https:\/\/doi.org\/10.1057\/s41310-021-00129-x.","journal-title":"Int J Discl Gov"},{"issue":"3","key":"660_CR68","doi-asserted-by":"publisher","first-page":"1206","DOI":"10.18517\/ijaseit.12.3.14724","volume":"12","author":"M Aljabri","year":"2022","unstructured":"Aljabri M, Aljameel SS, Khan IU, Aslam N, Charouf SMB, Alzahrani N. Machine learning model for sentiment analysis of COVID-19 tweets. Int J Adv Sci Eng Inf Technol. 2022;12(3):1206\u201314. https:\/\/doi.org\/10.18517\/ijaseit.12.3.14724.","journal-title":"Int J Adv Sci Eng Inf Technol"},{"key":"660_CR69","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1007\/s13278-022-00877-w","volume":"12","author":"RS Patil","year":"2022","unstructured":"Patil RS, Kolhe SR. Supervised classifiers with TF-IDF features for sentiment analysis of Marathi tweets. Soc Netw Anal Min. 2022;12:51. https:\/\/doi.org\/10.1007\/s13278-022-00877-w.","journal-title":"Soc Netw Anal Min"},{"key":"660_CR70","unstructured":"Engagement rate: a metric you can count on. https:\/\/www.socialbakers.com\/blog\/1427-engagement-rate-a-metric-you-can-count-on. Accessed 27 Nov 2021."},{"issue":"2","key":"660_CR71","doi-asserted-by":"publisher","first-page":"65","DOI":"10.3390\/bdcc6020065","volume":"6","author":"N Yeasmin","year":"2022","unstructured":"Yeasmin N, Mahbub NI, Baowaly MK, Singh BC, Alom Z, Aung Z, Azim MA. Analysis and prediction of user sentiment on COVID-19 pandemic using tweets. Big Data Cogn Comput. 2022;6(2):65. https:\/\/doi.org\/10.3390\/bdcc6020065.","journal-title":"Big Data Cogn Comput"},{"issue":"3","key":"660_CR72","doi-asserted-by":"publisher","first-page":"1","DOI":"10.4018\/IJBAN.292056","volume":"9","author":"M Daradkeh","year":"2022","unstructured":"Daradkeh M. Analyzing sentiments and diffusion characteristics of COVID-19 vaccine misinformation topics in social media: a data analytics framework. Int J Bus Anal. 2022;9(3):1\u201322. https:\/\/doi.org\/10.4018\/IJBAN.292056.","journal-title":"Int J Bus Anal"},{"key":"660_CR73","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1007\/s13278-022-00885-w","volume":"12","author":"S Mishra","year":"2022","unstructured":"Mishra S, Verma A, Meena K, et al. Public reactions towards Covid-19 vaccination through twitter before and after second wave in India. Soc Netw Anal Min. 2022;12:57. https:\/\/doi.org\/10.1007\/s13278-022-00885-w.","journal-title":"Soc Netw Anal Min"},{"key":"660_CR74","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2022.107967","volume":"101","author":"C Iwendi","year":"2022","unstructured":"Iwendi C, Mohan S, Khan S, Ibeke E, Ahmadian A, Ciano T. Covid-19 fake news sentiment analysis. Comput Electr Eng. 2022;101: 107967. https:\/\/doi.org\/10.1016\/j.compeleceng.2022.107967.","journal-title":"Comput Electr Eng"},{"key":"660_CR75","doi-asserted-by":"publisher","DOI":"10.1186\/s12889-020-8342-4","author":"A Porreca","year":"2020","unstructured":"Porreca A, Scozzari F, Di Nicola M. Using text mining and sentiment analysis to analyze YouTube Italian videos concerning vaccination. BMC Public Health. 2020. https:\/\/doi.org\/10.1186\/s12889-020-8342-4.","journal-title":"BMC Public Health"},{"issue":"10","key":"660_CR76","doi-asserted-by":"publisher","first-page":"1059","DOI":"10.3390\/vaccines9101059","volume":"9","author":"A Karami","year":"2021","unstructured":"Karami A, Zhu M, Goldschmidt B, Boyajieff HR, Najafabadi MM. COVID-19 vaccine and social media in the US: exploring emotions and discussions on Twitter. Vaccines. 2021;9(10):1059. https:\/\/doi.org\/10.3390\/vaccines9101059.","journal-title":"Vaccines"},{"key":"660_CR77","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2021.107526","author":"A Nasir","year":"2021","unstructured":"Nasir A, Ali Shah M, Ashraf U, Khan A, Jeon G. An intelligent framework to predict socio-economic impacts of COVID-19 and public sentiments. Comput Electr Eng. 2021. https:\/\/doi.org\/10.1016\/j.compeleceng.2021.107526.","journal-title":"Comput Electr Eng"},{"key":"660_CR78","doi-asserted-by":"publisher","DOI":"10.1007\/s13278-021-00737-z","author":"M Singh","year":"2021","unstructured":"Singh M, Jakhar AK, Pandey S. Sentiment analysis on the impact of coronavirus in social life using the BERT model. Soc Netw Anal Min. 2021. https:\/\/doi.org\/10.1007\/s13278-021-00737-z.","journal-title":"Soc Netw Anal Min"},{"key":"660_CR79","doi-asserted-by":"publisher","DOI":"10.1136\/postgradmedj-2021-140685","author":"R Marcec","year":"2021","unstructured":"Marcec R, Likic R. Using Twitter for sentiment analysis towards AstraZeneca\/Oxford, Pfizer\/BioNTech and Moderna COVID-19 vaccines. Postgrad Med J. 2021. https:\/\/doi.org\/10.1136\/postgradmedj-2021-140685.","journal-title":"Postgrad Med J"},{"issue":"43","key":"660_CR80","doi-asserted-by":"publisher","first-page":"6347","DOI":"10.1016\/j.vaccine.2021.09.030","volume":"39","author":"MA Sahraian","year":"2021","unstructured":"Sahraian MA, Ghadiri F, Azimi A, Moghadasi AN. Adverse events reported by Iranian patients with multiple sclerosis after the first dose of Sinopharm BBIBP-CorV. Vaccine. 2021;39(43):6347\u201350. https:\/\/doi.org\/10.1016\/j.vaccine.2021.09.030.","journal-title":"Vaccine"}],"container-title":["Journal of Big Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40537-022-00660-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s40537-022-00660-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40537-022-00660-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,22]],"date-time":"2022-11-22T23:07:02Z","timestamp":1669158422000},"score":1,"resource":{"primary":{"URL":"https:\/\/journalofbigdata.springeropen.com\/articles\/10.1186\/s40537-022-00660-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,21]]},"references-count":80,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["660"],"URL":"https:\/\/doi.org\/10.1186\/s40537-022-00660-w","relation":{},"ISSN":["2196-1115"],"issn-type":[{"value":"2196-1115","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,21]]},"assertion":[{"value":"29 November 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 October 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 November 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"110"}}