@InProceedings{xu-lan-wu:2018:S18-1,
  author    = {Xu, Huimin  and  Lan, Man  and  Wu, Yuanbin},
  title     = {ECNU at SemEval-2018 Task 1: Emotion Intensity Prediction Using Effective Features and Machine Learning Models},
  booktitle = {Proceedings of The 12th International Workshop on Semantic Evaluation},
  month     = {June},
  year      = {2018},
  address   = {New Orleans, Louisiana},
  publisher = {Association for Computational Linguistics},
  pages     = {231--235},
  abstract  = {This paper describes our submissions to SemEval 2018 task 1. The task is affect intensity prediction in tweets, including five subtasks. We participated in all subtasks of English tweets. We extracted several traditional NLP, sentiment lexicon, emotion lexicon and domain specific features from tweets, adopted supervised machine learning algorithms to perform emotion intensity prediction.},
  url       = {http://www.aclweb.org/anthology/S18-1035}
}