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dc.contributor.advisorChakrabarty, Amitabha
dc.contributor.authorRahman, Ramiz Ihteshamur
dc.contributor.authorKadir, Sk. Sajidul
dc.date.accessioned2018-02-22T05:21:50Z
dc.date.available2018-02-22T05:21:50Z
dc.date.copyright2017
dc.date.issued12/14/2017
dc.identifier.otherID 17241007
dc.identifier.otherID 17241006
dc.identifier.urihttp://hdl.handle.net/10361/9540
dc.descriptionCataloged from PDF version of thesis report.
dc.descriptionIncludes bibliographical references (pages 65-66).
dc.descriptionThis thesis report is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2017.en_US
dc.description.abstractMovies have been classified into genres since the inception of the medium. However, even till this day, the process of classifying movies into genres has been a manual, time consuming task requiring human expertise. Some work has been done in trying to automatically classify movies into genres using machine learning techniques and classifiers, and some success has been achieved. However, little work has been done in this field with regards to Indian movies specifically. In this paper, multiple supervised learning algorithms including Naïve Bayes, Logistic Regression, K Nearest Neighbor, Decision Tree and Linear SVM were used to classify a set of Indian movies including but not limited to Bollywood and South Indian movies. Naïve Bayes and Logistic Regression were found to be the better performers and K-Nearest Neighbors was the worst performer. Genres with high positive examples such as ‘Drama’ were classified correctly more often and 0.7 for precision and 0.7 for recall scores was obtained. Performance degraded drastically as the number of positive examples fell with the ‘Musical’ genre having precision scores close to 0.1 and recall scores nearing 0.en_US
dc.description.statementofresponsibilityRamiz Ihteshamur Rahman
dc.description.statementofresponsibilitySk. Sajidul Kadir
dc.format.extent66 pages
dc.language.isoenen_US
dc.publisherBRAC Universityen_US
dc.rightsBRAC University thesis reports are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.
dc.subjectGenre classificationen_US
dc.subjectMoviesen_US
dc.subjectSynopsisen_US
dc.titleGenre classification of movies using their synopsisen_US
dc.typeThesisen_US
dc.contributor.departmentDepartment of Computer Science and Engineering, BRAC University
dc.description.degreeB. Computer Science and Engineering


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