Hate Speech Detection on YouTube Using Long Short-Term Memory and Latent Dirichlet Allocation Method
-
Andi Fadil Adiyaksa
*
Telkom University, Bandung,
Indonesia
- Donny Richasdy Telkom University, Bandung, Indonesia
- Aditya Firman Ihsan Telkom University, Bandung, Indonesia (*) Corresponding Author
Abstract
YouTube social media is one of the popular media for all people to become a platform as a means of information and expressing opinions. Opinions can be categorized as hate if they attack something targeted. Hate speech is a behavior, word or action that is prohibited, because it causes violence to any individual and group. Expressing opinions in the form of hate speech is a problem that is still very difficult for the authorities to overcome because it is very common. Therefore, in this study a system was created to detect hate speech in the youtube comment column, using the Long Short-Term Memory and Latent Dirichlet Allocation. In this study, several methods were carried out that aimed to get the best accuracy value and carried out the topic modeling process using Latent Dirichlet Allocation to produce a total of three topics containing words that often appear in youtube comments. Based on the tests that have been obtained, the best accuracy is 0.657 or 66%.
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Pages: 644-650
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