@InProceedings{witon-EtAl:2018:WASSA2018,
  author    = {Witon, Wojciech  and  Colombo, Pierre  and  Modi, Ashutosh  and  Kapadia, Mubbasir},
  title     = {Disney at IEST 2018: Predicting Emotions using an Ensemble},
  booktitle = {Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis},
  month     = {October},
  year      = {2018},
  address   = {Brussels, Belgium},
  publisher = {Association for Computational Linguistics},
  pages     = {248--253},
  abstract  = {This paper describes our participating system in the WASSA 2018 shared task on emotion prediction. The task focusses on implicit emo- tion prediction in a tweet. In this task, key- words corresponding to the six emotion label names (anger, fear, disgust, joy, sad, and sur- prise ) have been removed from the tweet text, making emotion prediction implicit and the task challenging. We propose a model based on ensemble of classifiers for prediction. Each classifier in the ensemble uses sequence of Convolutional Neural Network (CNN) archi- tecture blocks and uses ELMo (Embeddings from Language Model) (Peters et al., 2018) as input. Our system achieves 66.2% F1 score on the test set. The best performing system in the shared task has reported 71.4% F1 score.},
  url       = {http://aclweb.org/anthology/W18-6236}
}