Hate Speech Detection for Indonesia Tweets Using Word Embedding And Gated Recurrent Unit


Junanda Patihullah(1*), Edi Winarko(2)

(1) Program Studi S2 Ilmu Komputer FMIPA UGM, Yogyakarta
(2) Department of Computer Science and Electronics, Faculty of Mathematics and Natural Sciences, Universitas Gadjah Mada
(*) Corresponding Author


Social media has changed the people mindset to express thoughts and moods. As the activity of social media users increases, it does not rule out the possibility of crimes of spreading hate speech can spread quickly and widely. So that it is not possible to detect hate speech manually. GRU is one of the deep learning methods that has the ability to learn information relations from the previous time to the present time. In this research feature extraction used is word2vec, because it has the ability to learn semantics between words. In this research the GRU performance will be compared with other supervision methods such as support vector machine, naive bayes, decision tree and logistic regression. The results obtained show that the best accuracy is 92.96% by the GRU model with word2vec feature extraction. The use of word2vec in the comparison supervision method is not good enough from tf and tf-idf.


Gated Recurrent Unit;Hate Speech Detection;Word2vec;RNN;Word Embedding

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DOI: https://doi.org/10.22146/ijccs.40125

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