{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T11:21:25Z","timestamp":1774610485537,"version":"3.50.1"},"reference-count":50,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2020,7,1]],"date-time":"2020-07-01T00:00:00Z","timestamp":1593561600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,7,1]],"date-time":"2020-07-01T00:00:00Z","timestamp":1593561600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100003093","name":"Ministry of Higher Education, Malaysia","doi-asserted-by":"crossref","award":["FRGS\/1\/2017\/ICT02\/UKM\/02\/4"],"award-info":[{"award-number":["FRGS\/1\/2017\/ICT02\/UKM\/02\/4"]}],"id":[{"id":"10.13039\/501100003093","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SN COMPUT. SCI."],"published-print":{"date-parts":[[2020,7]]},"DOI":"10.1007\/s42979-020-00249-1","type":"journal-article","created":{"date-parts":[[2020,7,16]],"date-time":"2020-07-16T15:04:22Z","timestamp":1594911862000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["A Survey on Machine Learning Techniques in Movie Revenue Prediction"],"prefix":"10.1007","volume":"1","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9514-1807","authenticated-orcid":false,"given":"Ibrahim Said","family":"Ahmad","sequence":"first","affiliation":[]},{"given":"Azuraliza Abu","family":"Bakar","sequence":"additional","affiliation":[]},{"given":"Mohd Ridzwan","family":"Yaakub","sequence":"additional","affiliation":[]},{"given":"Shamsuddeen Hassan","family":"Muhammad","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,7,16]]},"reference":[{"key":"249_CR1","doi-asserted-by":"crossref","unstructured":"Ahmad SR, Bakar AA, Yaakub MR. Metaheuristic algorithms for feature selection in sentiment analysis. In: 2015 science and information conference (SAI). IEEE; 2015. p. 222\u201326.","DOI":"10.1109\/SAI.2015.7237148"},{"issue":"3","key":"249_CR2","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1177\/0165551516683908","volume":"44","author":"T Al-Moslmi","year":"2018","unstructured":"Al-Moslmi T, Albared M, Al-Shabi A, Omar N, Abdullah S. Arabic senti-lexicon: constructing publicly available language resources for Arabic sentiment analysis. J Inf Sci. 2018;44(3):345\u201362.","journal-title":"J Inf Sci."},{"key":"249_CR3","doi-asserted-by":"crossref","first-page":"16173","DOI":"10.1109\/ACCESS.2017.2690342","volume":"5","author":"T Al-Moslmi","year":"2017","unstructured":"Al-Moslmi T, Omar N, Abdullah S, Albared M. Approaches to cross-domain sentiment analysis: a systematic literature review. IEEE Access. 2017;5:16173\u201392.","journal-title":"IEEE Access"},{"issue":"9","key":"249_CR4","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1186\/s12911-019-0990-x","volume":"19","author":"M Alloghani","year":"2019","unstructured":"Alloghani M, Aljaaf A, Hussain A, Baker T, Mustafina J, Al-Jumeily D, Khalaf M. Implementation of machine learning algorithms to create diabetic patient re-admission profiles. BMC Med Inform Decis Mak. 2019;19(9):253.","journal-title":"BMC Med Inform Decis Mak"},{"issue":"3","key":"249_CR5","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1057\/s41272-016-0072-y","volume":"16","author":"EA Antipov","year":"2017","unstructured":"Antipov EA, Pokryshevskaya EB. Are box office revenues equally unpredictable for all movies? Evidence from a random forest-based model. J Revenue Pricing Manag. 2017;16(3):295\u2013307.","journal-title":"J Revenue Pricing Manag"},{"key":"249_CR6","doi-asserted-by":"crossref","unstructured":"Asur S, Huberman BA. Predicting the future with social media. In: 2010 IEEE\/WIC\/ACM international conference on web intelligence and intelligent agent technology, vol. 1. 2010. p. 492\u2013499.","DOI":"10.1109\/WI-IAT.2010.63"},{"issue":"12","key":"249_CR7","doi-asserted-by":"crossref","first-page":"9207","DOI":"10.1007\/s00521-019-04248-z","volume":"31","author":"J Awwalu","year":"2019","unstructured":"Awwalu J, Bakar AA, Yaakub MR. Hybrid N-gram model using Na\u00efve Bayes for classification of political sentiments on Twitter. Neural Comput Appl. 2019;31(12):9207\u201320.","journal-title":"Neural Comput Appl"},{"issue":"3","key":"249_CR8","first-page":"344","volume":"24","author":"B Bhattacharjee","year":"2017","unstructured":"Bhattacharjee B, Sridhar A, Dutta A. Identifying the causal relationship between social media content of a Bollywood movie and its box-office success\u2014a text mining approach. Int J Bus Inf Syst. 2017;24(3):344\u201368.","journal-title":"Int J Bus Inf Syst"},{"issue":"15","key":"249_CR9","doi-asserted-by":"crossref","first-page":"4111","DOI":"10.2298\/FIL1615111C","volume":"30","author":"R Chen","year":"2016","unstructured":"Chen R, Xu W, Zhang X. Dynamic box office forecasting based on microblog data. Filomat. 2016;30(15):4111\u201324.","journal-title":"Filomat"},{"issue":"2","key":"249_CR10","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1007\/s10824-012-9198-y","volume":"38","author":"FW Derrick","year":"2014","unstructured":"Derrick FW, Williams NA, Scott CE. A two-stage proxy variable approach to estimating movie box office receipts. J Cult Econ. 2014;38(2):173\u201389.","journal-title":"J Cult Econ"},{"issue":"4 PART 2","key":"249_CR11","doi-asserted-by":"crossref","first-page":"1680","DOI":"10.1016\/j.eswa.2013.08.065","volume":"41","author":"J Du","year":"2014","unstructured":"Du J, Xu H, Huang X. Box office prediction based on microblog. Expert Syst Appl. 2014;41(4 PART 2):1680\u20139.","journal-title":"Expert Syst Appl"},{"issue":"4","key":"249_CR12","doi-asserted-by":"crossref","first-page":"1680","DOI":"10.1016\/j.eswa.2013.08.065","volume":"41","author":"J Du","year":"2014","unstructured":"Du J, Hua X, Huang X. Box office prediction based on microblog. Expert Syst Appl. 2014;41(4):1680\u20139.","journal-title":"Expert Syst Appl"},{"key":"249_CR13","first-page":"28","volume":"568","author":"J Duan","year":"2015","unstructured":"Duan J, Ding X, Liu T. \u201cA Gaussian copula regression model for movie box-office revenue prediction with social media\u201d edited by W. Z. H. X. Sun M. Zhang X. Commun Comput Inf Sci. 2015;568:28\u201337.","journal-title":"Commun Comput Inf Sci"},{"issue":"9","key":"249_CR14","doi-asserted-by":"crossref","first-page":"092103","DOI":"10.1007\/s11432-015-0905-6","volume":"60","author":"J Duan","year":"2017","unstructured":"Duan J, Ding X, Liu T. A Gaussian copula regression model for movie box-office revenues prediction. Sci China Inf Sci. 2017;60(9):092103.","journal-title":"Sci China Inf Sci"},{"issue":"9","key":"249_CR15","doi-asserted-by":"crossref","first-page":"1604","DOI":"10.1108\/IMDS-04-2015-0145","volume":"115","author":"DD Gaikar","year":"2015","unstructured":"Gaikar DD, Marakarkandy B, Dasgupta C. Using Twitter data to predict the performance of Bollywood movies. Ind Manag Data Syst. 2015;115(9):1604\u201321.","journal-title":"Ind Manag Data Syst"},{"issue":"6","key":"249_CR16","doi-asserted-by":"crossref","first-page":"3176","DOI":"10.1016\/j.eswa.2014.11.022","volume":"42","author":"M Ghiassi","year":"2015","unstructured":"Ghiassi M, Lio D, Moon B. Pre-production forecasting of movie revenues with a dynamic artificial neural network. Expert Syst Appl. 2015;42(6):3176\u201393.","journal-title":"Expert Syst Appl."},{"key":"249_CR17","first-page":"55","volume":"9489","author":"Z Guo","year":"2015","unstructured":"Guo Z, Zhang X, Hou Y. \u201cPredicting box office receipts of movies with pruned random forest\u201d edited by H. T. A. S. Lai W.K. Liu Q. Lect Notes Comput Sci (Incl Subser Lect Notes Artif Intell Lect Notes Bioinform). 2015;9489:55\u201362.","journal-title":"Lect Notes Comput Sci (Incl Subser Lect Notes Artif Intell Lect Notes Bioinform)"},{"key":"249_CR18","doi-asserted-by":"crossref","unstructured":"Guo Z, Zhang X, Hou Y. Predicting box office receipts of movies with pruned random forest. In: Neural information processing. ICONIP 2015. Lecture notes in computer science. Cham: Springer; 2015. p. 55\u201362.","DOI":"10.1007\/978-3-319-26532-2_7"},{"issue":"1","key":"249_CR19","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1108\/IJICC-04-2017-0033","volume":"11","author":"N Hossein","year":"2018","unstructured":"Hossein N, Miller DW. Predicting motion picture box office performance using temporal Tweet patterns. Int J Intell Comput Cybern. 2018;11(1):64\u201380.","journal-title":"Int J Intell Comput Cybern"},{"issue":"6","key":"249_CR20","doi-asserted-by":"crossref","first-page":"1010","DOI":"10.1108\/EL-02-2018-0040","volume":"36","author":"YH Hu","year":"2018","unstructured":"Hu YH, Shiau WM, Shih SP, Chen CJ. Considering online consumer reviews to predict movie box-office performance between the years 2009 and 2014 in the US. Electron Libr. 2018;36(6):1010\u201326.","journal-title":"Electron Libr"},{"key":"249_CR21","doi-asserted-by":"crossref","first-page":"608","DOI":"10.1016\/j.ins.2016.08.027","volume":"372","author":"M Hur","year":"2016","unstructured":"Hur M, Kang P, Cho S. Box-office forecasting based on sentiments of movie reviews and independent subspace method. Inf Sci. 2016;372:608\u201324.","journal-title":"Inf Sci"},{"key":"249_CR22","unstructured":"Keele S. Guidelines for performing systematic literature reviews in software engineering (Vol. 5). Technical report. 2015; Ver. 2.3 EBSE Technical Report. EBSE."},{"issue":"3\u20134","key":"249_CR23","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1080\/13614568.2013.835450","volume":"19","author":"D Kim","year":"2013","unstructured":"Kim D, Kim D, Hwang E, Choi HG. A user opinion and metadata mining scheme for predicting box office performance of movies in the social network environment. N Rev Hypermed Multimed. 2013;19(3\u20134):259\u201372.","journal-title":"N Rev Hypermed Multimed"},{"issue":"2","key":"249_CR24","doi-asserted-by":"crossref","first-page":"364","DOI":"10.1016\/j.ijforecast.2014.05.006","volume":"31","author":"T Kim","year":"2015","unstructured":"Kim T, Hong J, Kang P. Box office forecasting using machine learning algorithms based on SNS data. Int J Forecast. 2015;31(2):364\u201390.","journal-title":"Int J Forecast"},{"key":"249_CR25","doi-asserted-by":"crossref","unstructured":"Kim T, Hong J, Kang P. Box office forecasting considering competitive environment and word-of-mouth in social networks: a case study of korean film market. Comput Intell Neurosci. 2017.","DOI":"10.1155\/2017\/4315419"},{"key":"249_CR26","doi-asserted-by":"crossref","unstructured":"Lash MT, Fu S, Wang S, Zhao K. Early prediction of movie success what, who, and when. In: Social computing, behavioral-cultural modeling, and prediction. 2015. p. 345\u201349.","DOI":"10.1007\/978-3-319-16268-3_41"},{"issue":"3","key":"249_CR27","doi-asserted-by":"crossref","first-page":"874","DOI":"10.1080\/07421222.2016.1243969","volume":"33","author":"MT Lash","year":"2016","unstructured":"Lash MT, Zhao K. Early predictions of movie success: the who, what, and when of profitability. J Manag Inf Syst. 2016;33(3):874\u2013903.","journal-title":"J Manag Inf Syst"},{"issue":"3","key":"249_CR28","doi-asserted-by":"crossref","first-page":"577","DOI":"10.1007\/s10796-016-9689-z","volume":"20","author":"K Lee","year":"2018","unstructured":"Lee K, Park J, Kim I, Choi Y. Predicting movie success with machine learning techniques: ways to improve accuracy. Inf Syst Front. 2018;20(3):577\u201388.","journal-title":"Inf Syst Front"},{"issue":"5","key":"249_CR29","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1162\/REST_a_00671","volume":"99","author":"S Lehrer","year":"2017","unstructured":"Lehrer S, Xie T. Box office buzz: does social media data steal the show from model uncertainty when forecasting for Hollywood? Rev Econ Stat. 2017;99(5):749\u201355.","journal-title":"Rev Econ Stat"},{"key":"249_CR30","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.techfore.2016.05.013","volume":"109","author":"C Lipizzi","year":"2016","unstructured":"Lipizzi C, Iandoli L, Marquez JER. Combining structure, content and meaning in online social networks: the analysis of public\u2019s early reaction in social media to newly launched movies. Technol Forecast Soc Chang. 2016;109:35\u201349.","journal-title":"Technol Forecast Soc Chang"},{"issue":"3","key":"249_CR31","doi-asserted-by":"crossref","first-page":"1509","DOI":"10.1007\/s11042-014-2270-1","volume":"75","author":"T Liu","year":"2016","unstructured":"Liu T, Ding X, Chen Y, Chen H, Guo M. Predicting movie box-office revenues by exploiting large-scale social media content. Multimed Tools Appl. 2016;75(3):1509\u201328.","journal-title":"Multimed Tools Appl"},{"issue":"5","key":"249_CR32","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1109\/MCG.2014.61","volume":"34","author":"Y Lu","year":"2014","unstructured":"Lu Y, Wang F, Maciejewski R. Business intelligence from social media: a study from the VAST box office challenge. IEEE Comput Gr Appl. 2014;34(5):58\u201369.","journal-title":"IEEE Comput Gr Appl"},{"issue":"8","key":"249_CR33","doi-asserted-by":"crossref","first-page":"e71226","DOI":"10.1371\/journal.pone.0071226","volume":"8","author":"M Mestyan","year":"2013","unstructured":"Mestyan M, Yasseri T, Kertesz J. Early prediction of movie box office success based on wikipedia activity big data. PLOS ONE. 2013;8(8):e71226.","journal-title":"PLOS ONE"},{"issue":"1","key":"249_CR34","doi-asserted-by":"crossref","first-page":"1","DOI":"10.4018\/IJBAN.2018010101","volume":"5","author":"S Mohanty","year":"2018","unstructured":"Mohanty S, Clements N, Gupta V. Investigating the effect of ewom in movie box office success through an aspect-based approach. Int J Bus Anal. 2018;5(1):1\u201315.","journal-title":"Int J Bus Anal"},{"key":"249_CR35","doi-asserted-by":"crossref","first-page":"b2535","DOI":"10.1136\/bmj.b2535","volume":"339","author":"D Moher","year":"2009","unstructured":"Moher D, Liberati A, Tetzlaff J, Altman DG, PRISMA Group. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. BMJ (Clin Res Ed.). 2009;339:b2535.","journal-title":"BMJ (Clin Res Ed.)"},{"issue":"4","key":"249_CR36","first-page":"338","volume":"6","author":"C Oh","year":"2013","unstructured":"Oh C. Customer engagement, word-of-mouth and box office: the case of movie Tweets. Int J Inf Syst Change Manag. 2013;6(4):338\u201352.","journal-title":"Int J Inf Syst Change Manag"},{"issue":"1","key":"249_CR37","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.im.2016.03.004","volume":"54","author":"C Oh","year":"2017","unstructured":"Oh C, Roumani Y, Nwankpa JK, Hu HF. Beyond likes and Tweets: consumer engagement behavior and movie box office in social media. Inf Manag. 2017;54(1):25\u201337.","journal-title":"Inf Manag"},{"key":"249_CR38","doi-asserted-by":"crossref","unstructured":"Parimi R, Caragea D. Pre-release box-office success prediction for motion pictures. In: Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics). vol. 7988. LNAI; 2013. p. 571\u201385.","DOI":"10.1007\/978-3-642-39712-7_44"},{"issue":"18","key":"249_CR39","first-page":"4340","volume":"95","author":"S Park","year":"2017","unstructured":"Park S, Kim T. Forecasting audience of motion pictures considering competitive environment. J Theor Appl Inf Technol. 2017;95(18):4340\u20138.","journal-title":"J Theor Appl Inf Technol"},{"key":"249_CR40","doi-asserted-by":"crossref","unstructured":"Quader N, Gani MO, Chaki D, Ali MH. A machine learning approach to predict movie box-office success. In: 20th international conference of computer and information technology, ICCIT 2017, vols. 2018-Janua. Institute of Electrical and Electronics Engineers Inc.; 2018. p. 1\u20137","DOI":"10.1109\/ICCITECHN.2017.8281839"},{"key":"249_CR41","doi-asserted-by":"crossref","unstructured":"Quader N, Osman Gani Md, Chaki D, Haider Ali Md. A machine learning approach to predict movie box-office success. In: 2017 20th international conference of computer and information technology, ICCIT 2017. IEEE; 2017.","DOI":"10.1109\/ICCITECHN.2017.8281839"},{"issue":"23","key":"249_CR42","first-page":"77","volume":"77","author":"MT Riwinoto","year":"2015","unstructured":"Riwinoto MT, Zega SA, Irlanda G. Predicting animated film of box-office success with neural networks. J Teknol. 2015;77(23):77\u201382.","journal-title":"J Teknol"},{"key":"249_CR43","first-page":"182","volume":"52","author":"Y Ru","year":"2018","unstructured":"Ru Y, Bo Li, Liu J, Chai J. An effective daily box office prediction model based on deep neural networks. Cognit. Syst Res. 2018;52:182\u201391.","journal-title":"Syst Res"},{"key":"249_CR44","doi-asserted-by":"crossref","unstructured":"Ruhrl\u00e4nder RP, Boissier M, Uflacker M. Improving box office result predictions for movies using consumer-centric models. In: KDD \u201918 proceedings of the 24th ACM SIGKDD international conference on knowledge discovery and data mining, KDD \u201918. London: ACM New York, NY, USA; 2018. p. 655\u201364.","DOI":"10.1145\/3219819.3219840"},{"key":"249_CR45","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1007\/978-981-10-8633-5_2","volume":"709","author":"S Sachdev","year":"2018","unstructured":"Sachdev S, Agrawal A, Bhendarkar S, Prasad BR, Agarwal S. Movie box-office gross revenue estimation. Adv Intell Syst Comput. 2018;709:9\u201317.","journal-title":"Adv Intell Syst Comput"},{"key":"249_CR46","doi-asserted-by":"crossref","unstructured":"Wang W, Xiu J, Yang Z, Liu C. A deep learning model for predicting movie box office based on deep belief network, edited by S. Y. Tan Y. Tang Q. Advances in swarm intelligence. ICSI 2018. Lect Notes Comput Sci. 2018;10942:530\u201341.","DOI":"10.1007\/978-3-319-93818-9_51"},{"issue":"9","key":"249_CR47","doi-asserted-by":"crossref","first-page":"4809","DOI":"10.1007\/s00521-018-3731-7","volume":"31","author":"Y Wang","year":"2018","unstructured":"Wang Y, Ru Y, Chai J. Time series clustering based on sparse subspace clustering algorithm and its application to daily box-office data analysis. Neural Comput Appl. 2018;31(9):4809\u201318.","journal-title":"Neural Comput Appl"},{"issue":"1","key":"249_CR48","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1177\/1550147716684842","volume":"13","author":"J Xiao","year":"2017","unstructured":"Xiao J, Li X, Chen S, Zhao X. Meng Xu. An inside look into the complexity of box-office revenue prediction in China. Int J Distrib Sens Netw. 2017;13(1):1\u201314.","journal-title":"Int J Distrib Sens Netw."},{"key":"249_CR49","doi-asserted-by":"crossref","first-page":"495","DOI":"10.1016\/j.protcy.2013.12.220","volume":"11","author":"MR Yaakub","year":"2014","unstructured":"Yaakub MR, Li Y, Zhang J. Integration of sentiment analysis into customer relational model: the importance of feature ontology and synonym. Proc Technol. 2014;11:495\u2013501.","journal-title":"Proc Technol"},{"issue":"6","key":"249_CR50","doi-asserted-by":"crossref","first-page":"1855","DOI":"10.1007\/s00521-017-3162-x","volume":"31","author":"Y Zhou","year":"2017","unstructured":"Zhou Y, Zhang L, Yi Z. Predicting movie box-office revenues using deep neural networks. Neural Comput Appl. 2017;31(6):1855\u201365.","journal-title":"Neural Comput Appl"}],"container-title":["SN Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-020-00249-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s42979-020-00249-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-020-00249-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,4]],"date-time":"2023-10-04T09:28:07Z","timestamp":1696411687000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s42979-020-00249-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7]]},"references-count":50,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2020,7]]}},"alternative-id":["249"],"URL":"https:\/\/doi.org\/10.1007\/s42979-020-00249-1","relation":{},"ISSN":["2662-995X","2661-8907"],"issn-type":[{"value":"2662-995X","type":"print"},{"value":"2661-8907","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,7]]},"assertion":[{"value":"3 March 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 July 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 July 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with Ethical Standards"}},{"value":"On behalf of all authors, the corresponding author states that there is no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"235"}}