Deteksi Depresi dan Kecemasan Pengguna Twitter Menggunakan Bidirectional LSTM

TL;DR

This paper presents a Bidirectional LSTM model for detecting depression and anxiety in Twitter users, achieving high accuracy and addressing the challenge of identifying mental health issues from textual data.

Contribution

The study develops a Bidirectional LSTM model that outperforms traditional machine learning and standard LSTM models in detecting depression and anxiety from Twitter data.

Findings

BiLSTM achieved 94.12% accuracy.

The model effectively captures contextual information in text.

It surpasses traditional models in mental health detection accuracy.

Abstract

The most common mental disorders experienced by a person in daily life are depression and anxiety. Social stigma makes people with depression and anxiety neglected by their surroundings. Therefore, they turn to social media like Twitter for support. Detecting users with potential depression and anxiety disorders through textual data is not easy because they do not explicitly discuss their mental state. It takes a model that can identify potential users who experience depression and anxiety on textual data to get treatment earlier. Text classification techniques can achieve this. One approach that can be used is LSTM as an RNN architecture development in dealing with vanishing gradient problems. Standard LSTM does not capture enough information because it can only read sentences from one direction. Meanwhile, Bidirectional LSTM (BiLSTM) is a two-way LSTM that can capture information without ignoring the context and meaning of a sentence. The proposed BiLSTM model is higher than all traditional machine learning models and standard LSTMs. Based on the test results, the highest accuracy obtained by BiLSTM reached 94.12%. This study has succeeded in developing a model for the detection of depression and anxiety in Twitter users.