%0 Conference Proceedings %T EPUTION at SemEval-2018 Task 2: Emoji Prediction with User Adaption %A Zhou, Liyuan %A Xu, Qiongkai %A Suominen, Hanna %A Gedeon, Tom %Y Apidianaki, Marianna %Y Mohammad, Saif M. %Y May, Jonathan %Y Shutova, Ekaterina %Y Bethard, Steven %Y Carpuat, Marine %S Proceedings of the 12th International Workshop on Semantic Evaluation %D 2018 %8 June %I Association for Computational Linguistics %C New Orleans, Louisiana %F zhou-etal-2018-epution %X 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 problem explores the idea of transfer learning for adapting the classifier based on users’ tweeting 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 evidence for the rationality of enriching the original corpus longitudinally with user behaviors and transferring the lessons learned from corresponding users to specific instances. %R 10.18653/v1/S18-1071 %U https://aclanthology.org/S18-1071 %U https://doi.org/10.18653/v1/S18-1071 %P 449-453