@InProceedings{zhou-EtAl:2018:S18-1,
  author    = {Zhou, Liyuan  and  Xu, Qiongkai  and  Suominen, Hanna  and  Gedeon, Tom},
  title     = {EPUTION at SemEval-2018 Task 2: Emoji Prediction with User Adaption},
  booktitle = {Proceedings of The 12th International Workshop on Semantic Evaluation},
  month     = {June},
  year      = {2018},
  address   = {New Orleans, Louisiana},
  publisher = {Association for Computational Linguistics},
  pages     = {449--453},
  abstract  = {This paper describes our approach, called EPUTION, for the open trial of the SemEval- 2018 Task 2, Multilingual Emoji Prediction. The task relates to using social media — more precisely, Twitter — with its aim to predict the most likely associated emoji of a tweet. Our solution for this text classification prob- lem explores the idea of transfer learning for adapting the classifier based on users’ tweet- ing history. Our experiments show that our user-adaption method improves classification results by more than 6 per cent on the macro- averaged F1. Thus, our paper provides evi- dence for the rationality of enriching the orig- inal corpus longitudinally with user behaviors and transferring the lessons learned from cor- responding users to specific instances.},
  url       = {http://www.aclweb.org/anthology/S18-1071}
}