Depression Detection on Social Media X by Text  Analysis with Attention-based CNN-BiLSTM using  FastText - Dalam bentuk pengganti sidang - Artikel Jurnal

CHIKANDRA PERMATA ZAHIRA

Informasi Umum

Kode

25.04.459

Klasifikasi

000 - General Works

Jenis

Karya Ilmiah - Skripsi (S1) - Reference

Subjek

Data Science

Dilihat

110 kali

Informasi Lainnya

Abstraksi

The exponential growth of the digital domain has rendered contemporary society reliant on social media. Conse quently, the manner in which many individuals engage with social media can manifest indications of distress, such as depression. Social media X is a popular platform that can contain all the outpourings of its users called tweets. With the increasing cases of depression, it is important to be able to detect depression early. This research contributes to combining a hybrid deep learning method to detect depression on social media X with TF-IDF as a feature extraction that plays a role in measuring the importance of words in each user’s tweet, FastText as feature expansion to improve word representation and finding semantic similarities, and attention mechanisms as optimization in adding weights. With a total of 50,523 tweet data, a similarity corpus of 100,594 was constructed. Based on the result, using the attention mechanism the BiLSTM model achieved 84.25% accuracy, a 2.03% increase from the baselin