Depression Detection on Twitter Social Media Platform using Bidirectional Long-Short Term Memory
ANDRE AGASI SIMANUNGKALIT
Informasi Umum
Kode
23.04.2499
Klasifikasi
302.23 - Media of Communication, Mass Media
Jenis
Karya Ilmiah - Skripsi (S1) - Reference
Subjek
Natural Language Processing, Social Media-political Aspects,
Dilihat
377 kali
Informasi Lainnya
Abstraksi
<p>Depression is one of the mental disorders that are often experienced by a person in daily life. Social media platforms is a new thing as an alternative to tell stories and express current feelings by people today. Twitter is one of the social media that is often used to express feelings and opinions through tweets posts, including tweets that contain hate speech which indirectly shows symptoms of depressive disorder through statements uploaded. It also requires modeling that can recognize users with the potential to experience depression so that they can get initial treatment. This can be implemented using the BiLSTM (Bidirectional Long Short-Term Memory) method and the Word2Vec feature. It is also needs a modeling that can recognize the users who have the potential to experience depression so that they can get treatment at the beginning. This can be implemented using the BiLSTM (Bidirectional Long Short-Term Memory) method and the Word2Vec feature.</p>
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Koleksi & Sirkulasi
Tersedia 1 dari total 1 Koleksi
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Pengarang
| Nama | ANDRE AGASI SIMANUNGKALIT |
| Jenis | Perorangan |
| Penyunting | Warih Maharani, Prati Hutari Gani |
| Penerjemah |
Penerbit
| Nama | Universitas Telkom, S1 Informatika |
| Kota | Bandung |
| Tahun | 2023 |
Sirkulasi
| Harga sewa | IDR 0,00 |
| Denda harian | IDR 0,00 |
| Jenis | Non-Sirkulasi |
