@inproceedings{priban-martinek-2018-uwb,
    title = "{UWB} at {IEST} 2018: Emotion Prediction in Tweets with Bidirectional Long Short-Term Memory Neural Network",
    author = "P{\v{r}}ib{\'a}{\v{n}}, Pavel  and
      Mart{\'i}nek, Ji{\v{r}}{\'i}",
    editor = "Balahur, Alexandra  and
      Mohammad, Saif M.  and
      Hoste, Veronique  and
      Klinger, Roman",
    booktitle = "Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/W18-6232/",
    doi = "10.18653/v1/W18-6232",
    pages = "224--230",
    abstract = "This paper describes our system created for the WASSA 2018 Implicit Emotion Shared Task. The goal of this task is to predict the emotion of a given tweet, from which a certain emotion word is removed. The removed word can be \textit{sad}, \textit{happy}, \textit{disgusted}, \textit{angry}, \textit{afraid} or a synonym of one of them. Our proposed system is based on deep-learning methods. We use Bidirectional Long Short-Term Memory (BiLSTM) with word embeddings as an input. Pre-trained DeepMoji model and pre-trained emoji2vec emoji embeddings are also used as additional inputs. Our System achieves 0.657 macro F1 score and our rank is 13th out of 30."
}