Feature-Based Fusion Adversarial Recurrent Neural Networks for Text Sentiment Classification

Text sentiment classification is a fundamental task of natural language processing. In the past few years, many outstanding methods have attained favorable results in text sentiment classification. However, most of these methods do not make full use of the contextual information of the word embedding layer and attach less importance to the loss of information in the process of forwarding propagation. Hence, an ample room exists for further progress in enhancing the robustness of the model and the feature extraction of text. To tackle these problems, we propose a Feature-Based Fusion Adversarial Recurrent Neural Networks (FARNN-Att) integrated model with an attention mechanism. Firstly, we extract the long-term dependence of text using the BiLSTM network and put forward a novel contextual feature representation way. Subsequently, we combine the prediction results of two features vectors in the full connection layer, which can be captured through the feature connection and attention mechanism. Finally, a regularization method of adversarial training is used to improve the robustness and generalization ability of the model. Our proposed model was compared with other baseline methods such as TextCNN, BiLSTM, BiLSTM-Att, and RCNN on three different public datasets. The experimental results show that our model has state-of-the-art performance in text sentiment classification tasks in terms of accuracy, recall, and F1 score.