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dc.contributor.advisorSadeque, Farig
dc.contributor.advisorRahman, Rafeed
dc.contributor.authorSinha, Sumaiya
dc.contributor.authorNawar, Naharin Siddiqui
dc.contributor.authorKhan, Md. Abrar Faiaz
dc.date.accessioned2024-10-01T09:22:38Z
dc.date.available2024-10-01T09:22:38Z
dc.date.copyright©2024
dc.date.issued2024-05
dc.identifier.otherID 20101141
dc.identifier.otherID 24141298
dc.identifier.otherID 19301106
dc.identifier.urihttp://hdl.handle.net/10361/24270
dc.descriptionThis thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2024.en_US
dc.descriptionCataloged from PDF version of thesis.
dc.descriptionIncludes bibliographical references (pages 43-45).
dc.description.abstractOnline bullying has prevailed for years in the vast cesspool that is commonly known as the online social media. Increasing use of social media and online communication has led to a rise in cyberbullying– which is often facilitated by the abundant usage of code-mixing and code-switching. Research has been done to filter out these derogatory remarks. However, little research has been done on code-switched and code-mixed hateful remarks. English has blended into our Bangla language so effectively that people regularly use English letters to convey Bangla due to its convenience. English and Bangla are used interchangeably in regular conversations as well. Our main objective in this research is to detect these code-switched and code-mixed remarks– which we plan to do by taking advantage of the state-of-theart natural language processing technologies.en_US
dc.description.statementofresponsibilitySumaiya Sinha
dc.description.statementofresponsibilityNaharin Siddiqui Nawar
dc.description.statementofresponsibilityMd. Abrar Faiaz Khan
dc.format.extent53 pages
dc.language.isoenen_US
dc.publisherBrac Universityen_US
dc.rightsBrac University theses 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.subjectCyberbullyingen_US
dc.subjectCyber harassmenten_US
dc.subjectOnline bullyingen_US
dc.subjectSocial mediaen_US
dc.subjectHate speechen_US
dc.subjectNLPen_US
dc.subject.lcshNatural language processing (Computer science).
dc.subject.lcshAutomatic speech recognition.
dc.subject.lcshDeep learning (Machine learning).
dc.titleIdentifying code-mixed and code-switched hateful remarks on social media using NLPen_US
dc.typeThesisen_US
dc.contributor.departmentDepartment of Computer Science and Engineering, Brac University
dc.description.degreeB.Sc. in Computer Science


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