EmoSense at SemEval-2019 Task 3: Bidirectional LSTM Network for Contextual Emotion Detection in Textual Conversations
Sergey Smetanin · 2019
In this paper, we describe a deep-learning system for emotion detection in textual conversations that participated in SemEval-2019 Task 3 "EmoContext".We designed a specific architecture of bidirectional LSTM which allows not only to learn semantic and sentiment feature representation, but also to capture userspecific conversation features.To fine-tune word embeddings using distant supervision we additionally collected a significant amount of emotional texts.The system achieved 72.59% micro-average F 1 score for emotion classes on the test dataset, thereby significantly outperforming the officially-released baseline.Word embeddings and the source code were released for the research community.