Identifying code-mixed and code-switched hateful remarks on social media using NLP
| bracu.degree.level | Undergraduate | |
| bracu.type.group | Student Works | |
| datacite.rights | Open Access | |
| dc.contributor.advisor | Sadeque, Farig | |
| dc.contributor.advisor | Rahman, Rafeed | |
| dc.contributor.author | Sinha, Sumaiya | |
| dc.contributor.author | Nawar, Naharin Siddiqui | |
| dc.contributor.author | Khan, Md. Abrar Faiaz | |
| dc.contributor.department | Department of Computer Science and Engineering | |
| dc.date.accessioned | 2024-10-01T09:22:38Z | |
| dc.date.available | 2024-10-01T09:22:38Z | |
| dc.date.copyright | ©2024 | |
| dc.date.issued | 2024-05 | |
| dc.description | Cataloged from PDF version of thesis. | |
| dc.description | Includes bibliographical references (pages 43-45). | |
| dc.description | This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2024. | en_US |
| dc.description.abstract | Online 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.degree | Bachelor of Science in Computer Science | |
| dc.description.statementofresponsibility | Sumaiya Sinha | |
| dc.description.statementofresponsibility | Naharin Siddiqui Nawar | |
| dc.description.statementofresponsibility | Md. Abrar Faiaz Khan | |
| dc.format.extent | 53 pages | |
| dc.identifier.other | ID 20101141 | |
| dc.identifier.other | ID 24141298 | |
| dc.identifier.other | ID 19301106 | |
| dc.identifier.uri | http://hdl.handle.net/10361/24270 | |
| dc.language.iso | en | en_US |
| dc.publisher | BRAC University | en_US |
| dc.rights | Brac 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.subject | Cyberbullying | en_US |
| dc.subject | Cyber harassment | en_US |
| dc.subject | Online bullying | en_US |
| dc.subject | Social media | en_US |
| dc.subject | Hate speech | en_US |
| dc.subject | NLP | en_US |
| dc.subject.lcsh | Natural language processing (Computer science). | |
| dc.subject.lcsh | Automatic speech recognition. | |
| dc.subject.lcsh | Deep learning (Machine learning). | |
| dc.title | Identifying code-mixed and code-switched hateful remarks on social media using NLP | en_US |
| dc.type | Thesis | en_US |