Optimasi Analisis Sentimen Twitter Tentang Isu Kesehatan Mental dengan Bi-LSTM pada Dataset Tidak Berimbang - Fatmawati, Indah Rani; Putra, Muhammad Pajar Kharisma (Building of Informatics, Technology and Science (BITS), 2025)
This study aims to analyze Twitter user sentiments related to mental health issues using the Bidirectional Long Short-Term Memory (BiLSTM) model. The dataset consists of 52,681 entries covering seven mental health categories: Anxiety, Bipolar, Depression, Normal, Personality Disorder, Stress, and Suicidal. The methods used include data pre-processing, data splitting, and model training with class weight adjustment techniques to handle data imbalance. The training results show an increase in accuracy from 16.02% in the first epoch to 88.48% in the 10th epoch, with an evaluation accuracy of 74.21%. The model shows the best performance in the Anxiety class with an F1-score of 0.90. However, the model still experiences limitations in classifying minority classes such as Bipolar and Personality Disorder due to the small amount of data and the complexity of language expressions in these categories. Therefore, an increase in the amount of data and more adaptive language processing techniques are needed to improve model performance in categories with limited data.