TUGAS: Exploiting unlabelled data for Twitter sentiment analysis
Silvio Amir, Miguel Almeida, Bruno Martins, João Filgueiras, Mário J. Silva · 2014
This paper describes our participation in the message polarity classification task of SemEval 2014. We focused on exploiting unlabeled data to improve accuracy, combining features leveraging word representations 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.