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.