Sentiment Classification Using fastText Embedding and Deep Learning Model

Abstract

Along with the growing popularity of deep learning to handle classification problems, various deep learning models have emerged having complex architectures. The disadvantage of a model with a complex architecture is the computational problem in which takes longer training time than a simple model. The desire to take advantage of sentiment classification in real-time applications is the reason for using a simpler model architecture but still paying attention to the model performance. Furthermore, this study also investigates how fastText embedding affects the performance of a sentiment classification model. In this paper, we propose two sentiment classification models with simple architecture. The first model is the single-layered Bidirectional Gated Recurrent Unit (BiGRU) model with fastText embedding, and the second one is the single-layered Convolutional Neural Network (CNN) model with fastText embedding. Both models can provide competitive results compared to the baseline model that has previously been compared with other models with complex architectures. The best accuracy is produced by the fastText + CNN model, with 80% of accuracy for the MR dataset and 84% of accuracy for the SST2 dataset. This study shows that the use of CNN for sentiment classification can provide competitive results compared to the BiLSTM and BiGRU models. This study also indicates that the use of fastText embedding can improve the performance of the single-layered BiLSTM model.