@InProceedings{li-EtAl:2018:S18-1,
  author    = {Li, Meng  and  Dong, Zhenyuan  and  Fan, Zhihao  and  Meng, Kongming  and  Cao, Jinghua  and  Ding, Guanqi  and  Liu, Yuhan  and  Shan, Jiawei  and  Li, Binyang},
  title     = {ISCLAB at SemEval-2018 Task 1: UIR-Miner for Affect 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     = {286--290},
  abstract  = {This paper presents a UIR-Miner system for emotion and sentiment analysis evaluation in Twitter in SemEval 2018. Our system consists of three main modules: preprocessing module, stacking module to solve the intensity prediction of emotion and sentiment, LSTM network module to solve multi-label classification, and the hierarchical attention network module for solving emotion and sentiment classification problem. According to the metrics of SemEval 2018, our system gets the final scores of 0.636, 0.531, 0.731, 0.708, and 0.408 on 5 subtasks, respectively.},
  url       = {http://www.aclweb.org/anthology/S18-1042}
}