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Regular version of the site

Book chapter

EmoSense at SemEval-2019 Task 3: Bidirectional LSTM Network for Contextual Emotion Detection in Textual Conversations

P. 210-214.
Smetanin S.

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 user-specific 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 F1 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.

In book

Edited by: J. May, E. Shutova, A. Herbelot et al. Minneapolis: Association for Computational Linguistics, 2019.