{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T08:41:44Z","timestamp":1771231304284,"version":"3.50.1"},"reference-count":47,"publisher":"Emerald","issue":"5","license":[{"start":{"date-parts":[[2020,10,8]],"date-time":"2020-10-08T00:00:00Z","timestamp":1602115200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["DTA"],"published-print":{"date-parts":[[2020,10,8]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title><jats:p>Sequel movies are very popular; however, there are limited studies on sequel movie revenue prediction. The purpose of this paper is to propose a sentiment analysis based model for sequel movie revenue prediction and to propose a missing value imputation method for the sequel revenue prediction dataset.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title><jats:p>A sequel of a successful movie will most likely also be successful. Therefore, we propose a supervised learning approach in which data are created from sequel movies to predict the box-office revenue of an upcoming sequel. The algorithms used in the prediction are multiple linear regression, support vector machine and multilayer perceptron neural network.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Findings<\/jats:title><jats:p>The results show that using four sequel movies in a franchise to predict the box-office revenue of a fifth sequel achieved better prediction than using three sequels, which was also better than using two sequel movies.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Research limitations\/implications<\/jats:title><jats:p>The model produced will be beneficial to movie producers and other stakeholders in the movie industry in deciding the viability of producing a movie sequel.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title><jats:p>Previous studies do not give priority to sequel movies in movie revenue prediction. Additionally, a new missing value imputation method was introduced. Finally, sequel movie revenue prediction dataset was prepared.<\/jats:p><\/jats:sec>","DOI":"10.1108\/dta-10-2019-0180","type":"journal-article","created":{"date-parts":[[2020,10,12]],"date-time":"2020-10-12T03:03:56Z","timestamp":1602471836000},"page":"665-683","source":"Crossref","is-referenced-by-count":6,"title":["Sequel movie revenue prediction model based on sentiment analysis"],"prefix":"10.1108","volume":"54","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9514-1807","authenticated-orcid":false,"given":"Ibrahim Said","family":"Ahmad","sequence":"first","affiliation":[]},{"given":"Azuraliza","family":"Abu Bakar","sequence":"additional","affiliation":[]},{"given":"Mohd 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