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

Liyuan Zhou, Qiongkai Xu, Hanna Suominen, Tom Gedeon


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 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.
Anthology ID:
S18-1071
Volume:
Proceedings of The 12th International Workshop on Semantic Evaluation
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Venue:
*SEMEVAL
SIGs:
SIGLEX | SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
449–453
Language:
URL:
https://www.aclweb.org/anthology/S18-1071
DOI:
10.18653/v1/S18-1071
Bib Export formats:
BibTeX MODS XML EndNote
PDF:
http://aclanthology.lst.uni-saarland.de/S18-1071.pdf
Note:
 S18-1071.Notes.pdf