Bidirectional Long Short Term Memory Method and Word2vec Extraction Approach for Hate Speech Detection
Currently, the discussion about hate speech in Indonesia is warm, primarily through social media. Hate speech is communication that disparages a person or group based on characteristics such as (race, ethnicity, gender, citizenship, religion and organization). Twitter is one of the social media that...
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| Format: | EJournal Article |
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IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.,
2020-04-30.
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| LEADER | 02653 am a22003133u 4500 | ||
|---|---|---|---|
| 001 | IJCSS_51743 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a Isnain, Auliya Rahman |e author |
| 100 | 1 | 0 | |e contributor |
| 700 | 1 | 0 | |a Sihabuddin, Agus |e author |
| 700 | 1 | 0 | |a Suyanto, Yohanes |e author |
| 245 | 0 | 0 | |a Bidirectional Long Short Term Memory Method and Word2vec Extraction Approach for Hate Speech Detection |
| 260 | |b IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia., |c 2020-04-30. | ||
| 500 | |a https://jurnal.ugm.ac.id/ijccs/article/view/51743 | ||
| 520 | |a Currently, the discussion about hate speech in Indonesia is warm, primarily through social media. Hate speech is communication that disparages a person or group based on characteristics such as (race, ethnicity, gender, citizenship, religion and organization). Twitter is one of the social media that someone uses to express their feelings and opinions through tweets, including tweets that contain expressions of hatred because Twitter has a significant influence on the success or destruction of one's image.This study aims to detect hate speech or not hate Indonesian speech tweets by using the Bidirectional Long Short Term Memory method and the word2vec feature extraction method with Continuous bag-of-word (CBOW) architecture. For testing the BiLSTM purpose with the calculation of the value of accuracy, precision, recall, and F-measure.The use of word2vec and the Bidirectional Long Short Term Memory method with CBOW architecture, with epoch 10, learning rate 0.001 and the number of neurons 200 on the hidden layer, produce an accuracy rate of 94.66%, with each precision value of 99.08%, recall 93, 74% and F-measure 96.29%. In contrast, the Bidirectional Long Short Term Memory with three layers has an accuracy of 96.93%. The addition of one layer to BiLSTM increased by 2.27%. | ||
| 540 | |a Copyright (c) 2020 IJCCS (Indonesian Journal of Computing and Cybernetics Systems) | ||
| 540 | |a http://creativecommons.org/licenses/by-sa/4.0 | ||
| 546 | |a eng | ||
| 690 | |a Computer Science | ||
| 690 | |a Hate Speech; LSTM; BiLSTM; Word2vec; CBOW; Skipgram; Twitter | ||
| 655 | 7 | |a info:eu-repo/semantics/article |2 local | |
| 655 | 7 | |a info:eu-repo/semantics/publishedVersion |2 local | |
| 655 | 7 | |2 local | |
| 786 | 0 | |n IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 14, No 2 (2020): April; 169-178 | |
| 786 | 0 | |n 2460-7258 | |
| 786 | 0 | |n 1978-1520 | |
| 787 | 0 | |n https://jurnal.ugm.ac.id/ijccs/article/view/51743/27886 | |
| 856 | 4 | 1 | |u https://jurnal.ugm.ac.id/ijccs/article/view/51743 |z Get Fulltext |
| 856 | 4 | 1 | |u https://jurnal.ugm.ac.id/ijccs/article/view/51743/27886 |z Get Fulltext |