EPUTION at SemEval-2018 Task 2: Emoji Prediction with User Adaption

• DOI: 10.18653/v1/s18-1071
• Publication Date: 2018-05-30T00:39:56Z
• Authors: Liyuan Zhou; Qiongkai Xu; Hanna Suominen; Tom Gedeon
• Keywords: 01 natural sciences; 0105 earth and related environmental sciences

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.