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Cyberbullying detection using sentiment analysis in social media

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dc.contributor.advisor Mostakim, Moin Sintaha, Mifta Satter, Shahed Bin Zawad, Niamat Swarnaker, Chaity Hassan, Ahanaf 2016-09-20T05:18:10Z 2016-09-20T05:18:10Z 2016 2016-08-18
dc.identifier.other ID 13101123
dc.identifier.other ID 13101258
dc.identifier.other ID 13101283
dc.identifier.other ID 13101290
dc.identifier.other ID 13101002
dc.description This 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.description Cataloged from PDF version of thesis report.
dc.description Includes bibliographical references (page 47-49).
dc.description.abstract In 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.statementofresponsibility Mifta Sintaha
dc.description.statementofresponsibility Shahed Bin Satter
dc.description.statementofresponsibility Niamat Zawad
dc.description.statementofresponsibility Chaity Swarnaker
dc.description.statementofresponsibility Ahanaf Hassan
dc.format.extent 49 pages
dc.language.iso en en_US
dc.publisher BRAC University en_US
dc.rights BRAC 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.subject Support vector machine en_US
dc.subject RBF kernel en_US
dc.subject Cyberbullying detection en_US
dc.subject Social media en_US
dc.title Cyberbullying detection using sentiment analysis in social media en_US
dc.type Thesis en_US
dc.contributor.department Department of Computer Science and Engineering, BRAC University B. Computer Science and Engineering

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