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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.
Sergey Smetanin (Tue,) studied this question.