@InProceedings{chi-EtAl:2018:S18-1,
  author    = {Chi, Zewen  and  Huang, Heyan  and  Chen, Jiangui  and  Wu, Hao  and  Wei, Ran},
  title     = {Zewen at SemEval-2018 Task 1: An Ensemble Model for Affect Prediction in Tweets},
  booktitle = {Proceedings of The 12th International Workshop on Semantic Evaluation},
  month     = {June},
  year      = {2018},
  address   = {New Orleans, Louisiana},
  publisher = {Association for Computational Linguistics},
  pages     = {313--318},
  abstract  = {This paper presents a method for Affect in Tweets, which is the task to automatically determine the intensity of emotions and intensity of sentiment of tweets. The term affect refers to emotion-related categories such as anger, fear, etc. Intensity of emo-tions need to be quantified into a real valued score in [0, 1]. We propose an en-semble system including four different deep learning methods which are CNN, Bidirectional LSTM (BLSTM), LSTM-CNN and a CNN-based Attention model (CA). Our system gets an average Pearson correlation score of 0.682 in the subtask EI-reg and an average Pearson correlation score of 0.784 in subtask V-reg, which ranks 17th among 48 systems in EI-reg and 19th among 38 systems in V-reg.},
  url       = {http://www.aclweb.org/anthology/S18-1046}
}