Abstract
Nowadays, social media has permitted an exponential increase in the circulation of hostile and toxic content, which has resulted in an increase in the number of people exposed to it. Many members of the Natural Language Processing community have recently expressed an interest in automated identification of such harmful content as hate speech, provocative language, and abusive language as a means of addressing this problem. Machine learning and multilingual transformer models are used in this study to automatically identify Tamil language comments as either offensive or not offensive messages. The dataset is collected from YouTube and Kaggle. BERT tops the competition when it comes to offensive language identification models, with an accuracy of 82% compared to the others.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
