Deep learning for affective computing: text-based emotion recognition in decision support
Table 4: Holistic comparison of traditional machine learning and recurrent neural networks (with optional GloVe word embeddings) for affective computing, that is, models as regression tasks. Here the outcome variable represents the intensity according to predefined affective dimensions. Accordingly, the performance is measured based on the mean squared error (MSE); i. e., the lower the better. The best-performing model for each dataset is highlighted in bold. The percentage changes refer to the relative improvement over the best baseline from traditional machine learning. We point out that the first task exhibits higher errors due to the different scale of the outcome variable.
['[BOLD] Dataset', '[BOLD] Scale', '[BOLD] Baseline: Traditional machine learning  [BOLD] Random forest', '[BOLD] Baseline: Traditional machine learning  [BOLD] SVM 8.535827pt', '[BOLD] Deep learning  [BOLD] LSTM', '[BOLD] Deep learning  [BOLD] BiLSTM 8.535827pt', '[BOLD] Pre-trained word embeddings  [BOLD] LSTM', '[BOLD] Pre-trained word embeddings  [BOLD] BiLSTM']
These show a consistent improvement of up to 11.6\char37 as a result of using deep learning as compared to traditional machine learning. Similar to the classification task, our findings identify the BiLSTM with pre-trained word embeddings as the superior method in all seven experiments. We further note that the BiLSTM appears to outperform the unidirectional LSTM in all experiments. The relative performance increases vary between the different affective dimensions.