Deep learning for affective computing: text-based emotion recognition in decision support
Table 3: Additional comparison of sensitivity and specificity. The highest values for both are highlighted for each dataset.
['[BOLD] Dataset', '[BOLD] Baseline: traditional machine learning  [BOLD] Random forest', '[BOLD] Baseline: traditional machine learning  [BOLD] SVM', '[BOLD] Deep learning  [BOLD] LSTM', '[BOLD] Deep learning  [BOLD] BiLSTM', '[BOLD] Pre-trained word embeddings  [BOLD] LSTM', '[BOLD] Pre-trained word embeddings  [BOLD] BiLSTM']
The results confirm our findings, i. e., we witness the largest performance improvements for datasets with less noise. For the election tweet dataset