  Automatic identification of emotions expressed in Twitter data has a wide
range of applications. We create a well-balanced dataset by adding a neutral
class to a benchmark dataset consisting of four emotions: fear, sadness, joy,
and anger. On this extended dataset, we investigate the use of Support Vector
Machine (SVM) and Bidirectional Encoder Representations from Transformers
(BERT) for emotion recognition. We propose a novel ensemble model by combining
the two BERT and SVM models. Experiments show that the proposed model achieves
a state-of-the-art accuracy of 0.91 on emotion recognition in tweets.
