Three-way enhanced convolutional neural networks for sentence-level sentiment classification

Abstract Deep neural network models have achieved remarkable results in sentiment classification. Traditional feature-based methods perform slightly worse than deep learning methods in terms of classification accuracy, but they have their own advantages in interpretability and time complexity. To the best of our knowledge, few works study the ensemble of deep learning methods and traditional feature-based methods. Inspired by the methodology of three-way decisions, we proposed a three-way enhanced convolutional neural network model named 3W-CNN. 3W-CNN can be seen as an ensemble method which uses the enhance model to optimize convolutional neural networks (CNN). The enhance model is selected according to the classification accuracy and the difference in classification results compared to CNN. Support vector machine with naive bayes features (NB-SVM) is selected as the enhance model after comparing with several baseline models. However, the performance of NB-SVM is worse than CNN on most of benchmark datasets. To address this issue, we construct a component named confidence divider and design a confidence function to distinguish the classification quality of CNN. NB-SVM is further utilized to reclassify the predictions with weak confidence. The experimental results validated the effectiveness of 3W-CNN and showed three-way decisions could further improve the accuracy of sentiment classification.