Deteksi Depresi dan Kecemasan Pengguna Twitter Menggunakan Bidirectional LSTM
This addresses the problem of early detection for individuals with mental disorders who use social media, but it is incremental as it applies an existing method to a specific domain.
The paper tackled detecting depression and anxiety in Twitter users using textual data, achieving a highest accuracy of 94.12% with a Bidirectional LSTM model that outperformed traditional machine learning and standard LSTM approaches.
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.