@inproceedings{smetanin-2019-emosense,
    title = "{E}mo{S}ense at {S}em{E}val-2019 Task 3: Bidirectional {LSTM} Network for Contextual Emotion Detection in Textual Conversations",
    author = "Smetanin, Sergey",
    editor = "May, Jonathan  and
      Shutova, Ekaterina  and
      Herbelot, Aurelie  and
      Zhu, Xiaodan  and
      Apidianaki, Marianna  and
      Mohammad, Saif M.",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/S19-2034/",
    doi = "10.18653/v1/S19-2034",
    pages = "210--214",
    abstract = "In this paper, we describe a deep-learning system for emotion detection in textual conversations that participated in SemEval-2019 Task 3 {\textquotedblleft}EmoContext{\textquotedblright}. 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."
}