Deep learning for affective computing: text-based emotion recognition in decision support
Table 5: The numerical results show that transfer learning can yield additional performance improvements based on an inductive knowledge transfer across tasks (as opposed to the conventional strategy across datasets). In our sent2affect method, the neural networks are first trained on a sentiment analysis dataset in order to learn an abstract representation of emotion-laden text, while the final dense layer is subsequently replaced and fine-tuned using the task-specific dataset. Performance is measured in terms of F1-score; i. e., the higher 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 without transfer learning.
['[BOLD] Dataset', '[BOLD] Naïve BiLSTM', '[BOLD] BiLSTM (pre-trained embeddings)', '[BOLD] Transfer learning sent2affect']
Furthermore, naïve deep learning alone yields an inferior performance. While the BiLSTM with pre-trained word embeddings has previously represented the best-performing architecture, we still observe that transfer learning yields additional improvements. These amount to 6.6\char37 for the election tweets and 5.6\char37 for the general tweets. Evidently, transfer learning can successfully benefit from the large-scale dataset for sentiment analysis and, as a result, optimizes the neuron weights such that these find a more generalizable representation of emotion-laden materials.