Hate Speech Detection on Social Media using Bidirectional Long Short Term Memory (BiLSTM)
ISMI NUR AZIZA
Informasi Umum
Kode
23.05.067
Klasifikasi
006.31 - Machine Learning
Jenis
Karya Ilmiah - Thesis (S2) - Reference
Subjek
Machine Learning
Dilihat
287 kali
Informasi Lainnya
Abstraksi
<p style="margin-left:1.0cm;">The uses of Social Media give some benefits for human to interaction in society. But, sometimes the information dissemination carried out by an individual or group cannot be justified and causes abuse on social media. This Research is about detecting hatespeech on social media, especially Instagram comment section and also Twitter using variant of Reccurent Neural Network, that is Bidirectional LSTM. Recurrent Neural Network is a type of neural network that contains loops, allowing information to be stored within the network. This objective of this research is to identifying hate speech on social media, especially instagram and twitter platform with several aspect such as Abusive, Individual, and Other. The result of classifying Hate Speech using LSTM and fasttext achieve 81% for f1 score and on aspect based hate speech, Abusive aspect achieve higher result 84% on f1 score.</p>
Koleksi & Sirkulasi
Tersedia 1 dari total 1 Koleksi
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Pengarang
| Nama | ISMI NUR AZIZA |
| Jenis | Perorangan |
| Penyunting | Arie Ardiyanti Suryani |
| Penerjemah |
Penerbit
| Nama | Universitas Telkom, S2 Informatika |
| Kota | Bandung |
| Tahun | 2023 |
Sirkulasi
| Harga sewa | IDR 0,00 |
| Denda harian | IDR 0,00 |
| Jenis | Non-Sirkulasi |
