SCIA at SemEval-2019 Task 3: Sentiment Analysis in Textual Conversations Using Deep Learning
Zinedine Rebiai, Simon Calmar Andersen, Antoine Debrenne, Victor Lafargue · 2019
In this paper we present our submission for SemEval-2019 Task 3: EmoContext.The task consisted of classifying a textual dialogue into one of four emotion classes: happy, sad, angry or others.Our approach tried to improve on multiple aspects, preprocessing with an emphasis on spell-checking and ensembling with four different models: Bi-directional contextual LSTM (BC-LSTM), categorical Bi-LSTM (CAT-LSTM), binary convolutional Bi-LSTM (BIN-LSTM) and Gated Recurrent Unit (GRU).On the leader-board, we submitted two systems that obtained a micro F1 score (F1µ) of 0.711 and 0.712.After the competition, we merged our two systems with ensembling, which achieved a F1µ of 0.7324 on the test dataset.