{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T23:41:13Z","timestamp":1780357273832,"version":"3.54.1"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2022,1,6]],"date-time":"2022-01-06T00:00:00Z","timestamp":1641427200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,1,6]],"date-time":"2022-01-06T00:00:00Z","timestamp":1641427200000},"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":["Multimed Tools Appl"],"published-print":{"date-parts":[[2023,3]]},"DOI":"10.1007\/s11042-021-11553-0","type":"journal-article","created":{"date-parts":[[2022,1,6]],"date-time":"2022-01-06T21:11:51Z","timestamp":1641503511000},"page":"9597-9626","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Predicting attributes based movie success through ensemble machine learning"],"prefix":"10.1007","volume":"82","author":[{"given":"Vedika","family":"Gupta","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nikita","family":"Jain","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Harshit","family":"Garg","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Srishti","family":"Jhunthra","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Senthilkumar","family":"Mohan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdullah Hisam","family":"Omar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0106-7050","authenticated-orcid":false,"given":"Ali","family":"Ahmadian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,1,6]]},"reference":[{"issue":"1\u20132","key":"11553_CR1","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1023\/A:1007515423169","volume":"36","author":"E Bauer","year":"1999","unstructured":"Bauer E, Kohavi R (1999) An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Mach Learn 36(1\u20132):105\u2013139","journal-title":"Mach Learn"},{"key":"11553_CR2","doi-asserted-by":"crossref","unstructured":"Bhave A, Kulkarni H, Biramane V, Kosamkar P (2015) Role of different factors in predicting movie success. In: 2015 International Conference on Pervasive Computing (ICPC). IEEE, pp 1\u20134","DOI":"10.1109\/PERVASIVE.2015.7087152"},{"key":"11553_CR3","unstructured":"Chen T, He T, Benesty M, Khotilovich V, Tang Y (2015) Xgboost: extreme gradient boosting. R package version 0.4\u20132, 1\u20134"},{"key":"11553_CR4","doi-asserted-by":"publisher","first-page":"110713","DOI":"10.1016\/j.chaos.2021.110713","volume":"144","author":"AK Das","year":"2021","unstructured":"Das AK, Kalam S, Kumar C, Sinha D (2021) TLCoV-An automated Covid-19 screening model using transfer learning from chest X-ray images. Chaos Solitons Fractals 144:110713","journal-title":"Chaos Solitons Fractals"},{"key":"11553_CR5","doi-asserted-by":"crossref","unstructured":"Dhir R, Raj A (2018) Movie success prediction using machine learning algorithms and their comparison. In: 2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC), IEEE, pp 385\u2013390","DOI":"10.1109\/ICSCCC.2018.8703320"},{"key":"11553_CR6","unstructured":"Dietterich TG (2002) Ensemble learning. The handbook of brain theory and neural networks 2:110-125"},{"issue":"4","key":"11553_CR7","doi-asserted-by":"publisher","first-page":"6423","DOI":"10.1016\/j.sbspro.2010.04.052","volume":"2","author":"L Doshi","year":"2010","unstructured":"Doshi L, Krauss J, Nann S, Gloor P (2010) Predicting movie prices through dynamic social network analysis. Procedia Soc Behav Sci 2(4):6423\u20136433","journal-title":"Procedia Soc Behav Sci"},{"issue":"7\u20139","key":"11553_CR8","doi-asserted-by":"publisher","first-page":"1483","DOI":"10.1016\/j.neucom.2008.11.026","volume":"72","author":"PJ Garc\u00eda-Laencina","year":"2009","unstructured":"Garc\u00eda-Laencina PJ, Sancho-G\u00f3mez JL, Figueiras-Vidal AR, Verleysen M (2009) K nearest neighbours with mutual information for simultaneous classification and missing data imputation. Neurocomputing 72(7\u20139):1483\u20131493","journal-title":"Neurocomputing"},{"issue":"3","key":"11553_CR9","first-page":"308","volume":"3","author":"V Jain","year":"2013","unstructured":"Jain V (2013) Prediction of movie success using sentiment analysis of tweets. Int J Soft Comput Softw Eng 3(3):308\u2013313","journal-title":"Int J Soft Comput Softw Eng"},{"key":"11553_CR10","unstructured":"Jernb\u00e4cker C, Pojan S (2017) Predicting movie success using machine learning techniques"},{"key":"11553_CR11","doi-asserted-by":"crossref","unstructured":"Kanitkar A (2018). Bollywood movie success prediction using machine learning algorithms. In: 2018 3rd International conference on circuits, control, communication and computing (I4C). IEEE, pp 1\u20134","DOI":"10.1109\/CIMCA.2018.8739693"},{"key":"11553_CR12","doi-asserted-by":"crossref","unstructured":"Kumar V, Das AK, Sinha D (2019) UIDS: a unified intrusion detection system for IoT environment. Evol Intell 1\u201313","DOI":"10.1007\/s12065-019-00291-w"},{"issue":"3","key":"11553_CR13","doi-asserted-by":"publisher","first-page":"874","DOI":"10.1080\/07421222.2016.1243969","volume":"33","author":"MT Lash","year":"2016","unstructured":"Lash MT, Zhao K (2016) Early predictions of movie success: the who, what, and when of profitability. J Manag Inf Syst 33(3):874\u2013903","journal-title":"J Manag Inf Syst"},{"issue":"8","key":"11553_CR14","first-page":"127","volume":"16","author":"MH Latif","year":"2016","unstructured":"Latif MH, Afzal H (2016) Prediction of movies popularity using machine learning techniques. Int J Comput Sci Netw Sec 16(8):127","journal-title":"Int J Comput Sci Netw Sec"},{"issue":"3","key":"11553_CR15","first-page":"18","volume":"2","author":"A Liaw","year":"2002","unstructured":"Liaw A, Wiener M (2002) Classification and regression by randomForest. R News 2(3):18\u201322","journal-title":"R News"},{"key":"11553_CR16","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1016\/j.enconman.2014.12.053","volume":"92","author":"H Liu","year":"2015","unstructured":"Liu H, Tian HQ, Li YF, Zhang L (2015) Comparison of four Adaboost algorithm based artificial neural networks in wind speed predictions. Energy Convers Manage 92:67\u201381","journal-title":"Energy Convers Manage"},{"key":"11553_CR17","doi-asserted-by":"crossref","unstructured":"Modi A, George EL (2020) Genre-based indian viewer movie reviews\u2014A sentiment analysis classification of text and emoticons with a supervised machine learning approach. In: Advanced computing technologies and applications. Springer, Singapore, pp 633\u2013644","DOI":"10.1007\/978-981-15-3242-9_60"},{"key":"11553_CR18","unstructured":"Nikita J, Gupta V, Shubham S, Madan A, Chaudhary A, Santosh KC (2021) Understanding cartoon emotion using integrated deep neural network on large dataset. Neural Comput Appl 1\u201321"},{"key":"11553_CR19","doi-asserted-by":"publisher","first-page":"103813","DOI":"10.1016\/j.rinp.2021.103813","volume":"21","author":"J Nikita","year":"2021","unstructured":"Nikita J, Jhunthra S, Garg H, Gupta V, Mohan S, Ahmadian A, Salahshour S, Ferrara M (2021) Prediction modelling of COVID using machine learning methods from B-cell dataset. Results Phys 21:103813","journal-title":"Results Phys"},{"key":"11553_CR20","first-page":"365","volume":"3","author":"VR Nithin","year":"2014","unstructured":"Nithin VR, Pranav M, Sarath B, Lijiya A (2014) Predicting movie success based on IMDb data. Int J Data Min Tech Appl 3:365\u2013368","journal-title":"Int J Data Min Tech Appl"},{"issue":"5","key":"11553_CR21","doi-asserted-by":"publisher","first-page":"3297","DOI":"10.3233\/JIFS-169272","volume":"32","author":"R Piryani","year":"2017","unstructured":"Piryani R, Gupta V, Singh VK (2017) Movie prism: a novel system for aspect level sentiment profiling of movies. J Intell Fuzzy Syst 32(5):3297\u20133311","journal-title":"J Intell Fuzzy Syst"},{"key":"11553_CR22","doi-asserted-by":"crossref","unstructured":"Piryani R, Gupta V. Singh VK, Ghose U (2017) A linguistic rule-based approach for aspect-level sentiment analysis of movie reviews. In: Advances in computer and computational sciences. Springer, Singapore, pp 201\u2013209","DOI":"10.1007\/978-981-10-3770-2_19"},{"key":"11553_CR23","doi-asserted-by":"crossref","unstructured":"Pradeep K, TintuRosmin CR, Durom SS, Anisha GS (2020). Decision tree algorithms for accurate prediction of movie rating. In: 2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC). IEEE, pp 853\u2013858","DOI":"10.1109\/ICCMC48092.2020.ICCMC-000158"},{"key":"11553_CR24","doi-asserted-by":"crossref","unstructured":"Quader N, Gani MO, Chaki D, Ali MH (2017) A machine learning approach to predict movie box-office success. In: 2017 20th International Conference of Computer and Information Technology (ICCIT). IEEE, pp 1\u20137","DOI":"10.1109\/ICCITECHN.2017.8281839"},{"key":"11553_CR25","doi-asserted-by":"crossref","unstructured":"Schlkopf B, Smola AJ, Bach F (2018) Learning with kernels: support vector machines, regularization, optimization, and beyond. MIT Press","DOI":"10.7551\/mitpress\/4175.001.0001"},{"key":"11553_CR26","doi-asserted-by":"crossref","unstructured":"Tzanos G, Kachris C, Soudris D (2019) Hardware acceleration on gaussian naive bayes machine learning algorithm. In: 2019 8th International Conference on Modern Circuits and Systems Technologies (MOCAST). IEEE, pp 1\u20135","DOI":"10.1109\/MOCAST.2019.8741875"},{"key":"11553_CR27","doi-asserted-by":"crossref","unstructured":"Verma H, Verma G (2020) Prediction model for bollywood movie success: a comparative analysis of performance of supervised machine learning algorithms. Rev Socionetw Stratg, 14(1): 1\u201317. (biggest limitation that the dataset was used was very small of about 200 movies)","DOI":"10.1007\/s12626-019-00040-6"},{"key":"11553_CR28","doi-asserted-by":"crossref","unstructured":"Watershed (Iran) using an artificial neural network model: a comparison between multilayer perceptron (MLP) and radial basic function (RBF) algorithms. Arab J Geosci 6(8):2873\u20132888","DOI":"10.1007\/s12517-012-0610-x"},{"key":"11553_CR29","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.jtbi.2017.01.019","volume":"417","author":"Q Xu","year":"2017","unstructured":"Xu Q, Xiong Y, Dai H, Kumari KM, Xu Q, Ou HY, Wei DQ (2017) PDC-SGB: Prediction of effective drug combinations using a stochastic gradient boosting algorithm. J Theor Biol 417:1\u20137","journal-title":"J Theor Biol"},{"key":"11553_CR30","unstructured":"Zare M, Pourghasemi HR, Vafakhah M, Pradhan B (2013) Landslide susceptibility mapping at Vaz"},{"key":"11553_CR31","doi-asserted-by":"crossref","unstructured":"Zhang W, Skiena S (2009) Improving movie gross prediction through news analysis. In: 2009 IEEE\/WIC\/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, vol. 1, IEEE, pp 301\u2013304","DOI":"10.1109\/WI-IAT.2009.53"},{"key":"11553_CR32","unstructured":"Zheng A, Casari A (2018) Feature engineering for machine learning: principles and techniques for data scientists. O'Reilly Media, Inc"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-021-11553-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-021-11553-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-021-11553-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,2]],"date-time":"2023-03-02T16:15:57Z","timestamp":1677773757000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-021-11553-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,6]]},"references-count":32,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2023,3]]}},"alternative-id":["11553"],"URL":"https:\/\/doi.org\/10.1007\/s11042-021-11553-0","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"value":"1380-7501","type":"print"},{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,6]]},"assertion":[{"value":"2 February 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 August 2021","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 September 2021","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 January 2022","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}