Paper: TUGAS: Exploiting unlabelled data for Twitter sentiment analysis

ACL ID S14-2120
Title TUGAS: Exploiting unlabelled data for Twitter sentiment analysis
Venue Joint Conference on Lexical and Computational Semantics
Session
Year 2014
Authors

This paper describes our participation in the message polarity classification task of SemEval 2014. We focused on exploiting unlabeled data to improve accuracy, com- bining features leveraging word represen- tations with other, more common features, based on word tokens or lexicons. We analyse the contribution of the different features, concluding that unlabeled data yields significant improvements.