Emotion Detection with Neural Personal Discrimination
Table 2: Experimental results of different models.
['[BOLD] Method', '[BOLD] F1. Happiness', '[BOLD] F1. Sadness', '[BOLD] F1. Anger', '[BOLD] F1. Surprise', '[BOLD] F1. Fear', '[BOLD] F1. Average']
We compare the proposed Neural Personal Discrimination (NPD) model with several representative baselines models in SVM is a widely used baseline to predict the emotion of a post in social media yang2007.2) Abdul17 is a standard LSTM model which consist of a LSTM layer and a fully connected layer, and it is modified from the model in \newciteAbdul2017EmoNet. The LSTM model yields the state-of-the-art performance on emotion detection in recent researches.3)Vaswani17 is an improved LSTM model with a self-attention mechanism. The self-attention mechanism is used to capture the structural information and has been successfully applied in various natural language processing tasks recently cheng2016long; This indicates that neural models are much more effective than discrete models in emotion detection. In addition, our proposed NPD model outperforms both the standard LSTM model (Abdul17) and the improved LSTM model with self-attention (Vaswani17) significantly. This shows the effectiveness of our proposed NPD model with both adversarial discriminators and attention mechanisms. This also shows the usefulness of personal attributes for emotion detection. Moreover, we find that the performance of Vaswani17 is even lower than the standard LSTM model. This shows that simply integrating a self-attention mechanism may not be able to well capture informative words for emotion detection in social media.