We propose a Long Short-Term Memory (LSTM) with attention mechanism to classify psychological stress from self-conducted interview transcriptions. We apply distant supervision by automatically labeling tweets based on their hashtag content, which complements and expands the size of our corpus. This additional data is used to initialize the model parameters, after which it is fine-tuned using the interview data. This improves the model robustness, especially by expanding the vocabulary size. The bidirectional LSTM model with attention is found to be the best model in terms of accuracy (74.1%) and f-score (74.3%). Furthermore, we show that distant supervision fine-tuning enhances the model performance by 1.6% accuracy and 2.1% f-score. The attention mechanism helps the model to select informative words.
Recommended citation: Winata et al. (2018). “Attention-Based LSTM for Psychological Stress Detection from Spoken Language Using Distant Supervision.” IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).