Efficient Transformer Based Sentiment Classification Models

Abstract

Recently, transformer models have gained significance as a state-of-the art technique for sentiment prediction based on text. Attention mechanism of transformer model speeds up the training process by allowing modelling of dependencies without regard to their distance in the input or output sequences. There are two types of transformer models – transformer base models and transformer large models. Since the implementation of large transformer models need better hardware and more training time, we propose new simpler models or weak learners with lower training time for sentiment classification in this work. These models enhance the speed of performance without compromising the classification accuracy. The proposed Efficient Transformer-based Sentiment Classification (ETSC) models are built by setting configuration of large models as minimum, shuffling dataset randomly and experimenting with various percentages of training data. Early stopping and smaller batch size in training techniques improve the accuracy of the proposed model. The proposed models exhibit promising performance in comparison with existing transformer-based sentiment classification models in terms of speed and accuracy.