@InProceedings{alomari-abdullah-bassam:2019:S19-2,
  author    = {Al-Omari, Hani  and  Abdullah, Malak  and  Bassam, Nabeel},
  title     = {EmoDet at SemEval-2019 Task 3: Emotion Detection in Text using Deep Learning},
  booktitle = {Proceedings of the 13th International Workshop on Semantic Evaluation},
  month     = {June},
  year      = {2019},
  address   = {Minneapolis, Minnesota, USA},
  publisher = {Association for Computational Linguistics},
  pages     = {200--204},
  abstract  = {Task 3, EmoContext, in the International Workshop SemEval 2019 provides training and testing datasets for the participant teams to detect emotion classes (Happy, Sad, Angry, or Others). This paper proposes a participating system (EmoDet) to detect emotions using deep learning architecture. The main input to the system is a combination of Word2Vec word embeddings and a set of semantic features (e.g. from AffectiveTweets Weka-package). The proposed system (EmoDet) ensembles a fully connected neural network architecture and LSTM neural network to obtain performance results that show substantial improvements (F1-Score 0.67) over the baseline model provided by Task 3 organizers (F1-score 0.58).},
  url       = {http://www.aclweb.org/anthology/S19-2032}
}