EPITA-ADAPT at SemEval-2019 Task 3: Detecting emotions in textual conversations using deep learning models combination
Messaging platforms like WhatsApp, Facebook Messenger and Twitter have gained recently much popularity owing to their ability in connecting users in real-time. The content of these textual messages can be a useful resource for text mining to discover and unhide various aspects, including emotions. In this paper we present our submission for SemEval 2019 task {`}EmoContext{'}. The task consists of classifying a given textual dialogue into one of four emotion classes: Angry, Happy, Sad and Others. Our proposed system is based on the combination of different deep neural networks techniques. In particular, we use Recurrent Neural Networks (LSTM, B-LSTM, GRU, B-GRU), Convolutional Neural Network (CNN) and Transfer Learning (TL) methodes. Our final system, achieves an F1 score of 74.51{\%} on the subtask evaluation dataset.
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