Sentylic at IEST 2018: Gated Recurrent Neural Network and Capsule Network Based Approach for Implicit Emotion Detection
Table 2: Performance analysis of the best models in each investigated approaches.
['[BOLD] Model', '[BOLD] Macro-F1']
Recurrent Neural Networks (RNN) Socher et al. RNNs have the capability to capture sequential features present in sentences. Further, when they are incorporated with attention mechanisms the accuracy of the models increases notably Yang et al. ; Tang et al. Hence, we have first implemented a model which uses a bidirectional GRU Cho et al. Attention mechanisms have the ability to capture important keywords in sentences and give a higher weight to those words. This is one of the prominent approaches that typically results in a good performance in regular text classification tasks. Intuition is that the CNN layers will act as a different attention mechanism and captures high-level features from the features learned by the below layers. Hence, the second approach we investigated was using CNNs instead of the attention mechanism.