KSU at SemEval-2019 Task 3: Hybrid Features for Emotion Recognition in Textual Conversation
Nourah Alswaidan, Mohamed El Bachir Menaï · 2019
In this paper, we present the model submitted to the SemEval-2019 Task 3 competition: contextual emotion detection in text "EmoContext".We propose a model that hybridizes automatically extracted features and human engineered features to capture the representation of a textual conversation from different perspectives.The proposed model utilizes a fast gated-recurrent-unit backed by CuDNN (CuDNNGRU), and a convolutional neural network (CNN) to automatically extract features.The human engineered features take the term frequency-inverse document frequency (TF-IDF) of semantic meaning and mood tags extracted from SinticNet.For the classification, a dense neural network (DNN) is used with a sigmoid activation function.The model achieved a micro-F1 score of 0.6717 on the test dataset.