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dc.contributor.advisorMostakim, Moin
dc.contributor.authorSintaha, Mifta
dc.contributor.authorSatter, Shahed Bin
dc.contributor.authorZawad, Niamat
dc.contributor.authorSwarnaker, Chaity
dc.contributor.authorHassan, Ahanaf
dc.date.accessioned2016-09-20T05:18:10Z
dc.date.available2016-09-20T05:18:10Z
dc.date.copyright2016
dc.date.issued2016-08-18
dc.identifier.otherID 13101123
dc.identifier.otherID 13101258
dc.identifier.otherID 13101283
dc.identifier.otherID 13101290
dc.identifier.otherID 13101002
dc.identifier.urihttp://hdl.handle.net/10361/6420
dc.descriptionThis thesis report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2016.en_US
dc.descriptionCataloged from PDF version of thesis report.
dc.descriptionIncludes bibliographical references (page 47-49).
dc.description.abstractIn this day and age, the usage of Social Media has increased enormously in our daily lives. People like to share their experiences in various social media accounts for their friends to see. Consequently, the possibility and growth of cyber threats have increased as well. To reduce this situation, we try to propose a system that can detect cyber crimes such as fraud, blackmail, spam, impersonation etc. from the social media network Twitter. This type of study can help people to detect early threats and possible criminal activity and the types of accounts to stay alert of in real time thereby creating a more secure social media experience. Our main goal is to compare various sentiment analysis approaches for detecting bullying or threats from social media using three different machine learning algorithms and form a comparison to determine which among the three gives out the highest accuracy in order for us to decide how to detect cyber bullying activity on the Internet and be alert of threats in both the real and virtual world.en_US
dc.description.statementofresponsibilityMifta Sintaha
dc.description.statementofresponsibilityShahed Bin Satter
dc.description.statementofresponsibilityNiamat Zawad
dc.description.statementofresponsibilityChaity Swarnaker
dc.description.statementofresponsibilityAhanaf Hassan
dc.format.extent49 pages
dc.language.isoenen_US
dc.publisherBRAC Universityen_US
dc.rightsBRAC University thesis 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.subjectSupport vector machineen_US
dc.subjectRBF kernelen_US
dc.subjectCyberbullying detectionen_US
dc.subjectSocial mediaen_US
dc.titleCyberbullying detection using sentiment analysis in social mediaen_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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