I2RNTU at SemEval-2016 task 4: Classifier fusion for polarity classification in twitter

Abstract

In this work, we apply classifier fusion to tweet polarity identification problem. The task is to predict whether the emotion hidden in a tweet is positive, neutral, or negative. An asymmetric SIMPLS (ASIMPLS) based classifier, which was proved to be able to identify the minority class well in imbalanced classification problems, is implemented. Word embedding is also employed as a new feature. For each word, we obtain three word embedding vectors on positive, neutral, and negative tweet sets respectively. These vectors are used as features in the ASIMPLS classifier. Another three state-of-the-art systems are implemented also, and these four systems are fused together to further boost the performance. The fusion system achieved 59.63% accuracy on the 2016 test set of SemEval2016 Task 4, Subtask A.