An Enhancement of Tree-Structured Deep Learning Classification Through Semantic Enabled Frequency Aware Data Augmentation

Abstract

Nowadays, tree-structured deep learning classifier models have been widely used in different applications to ensure effective feature representation and learning. Amongst, dimensional sentiment analysis is the most interactive research field, which intends to identify continuous numerical values in the valence-arousal (VA) space. To achieve this, a tree-structured regional convolutional neural network with long short-term memory (T-CNN-LSTM) model was developed, which predicts the VA ratings of the texts for sentiment analysis. In contrast, the effect of a low prediction rate and difficulty of feature learning in a small number of class samples was not analyzed. Hence, this manuscript proposes an adversarial T-CNN-LSTM (A-T-CNN-LSTM) model for predicting the VA to achieve more fine-grained sentiment analysis. This model develops a semantic-enabled frequency-aware generative adversarial network (SFGAN) to produce more adversarial samples using the generator network and decrease the spectral data loss of the discriminator. It embeds the frequency-aware categorizer (FAC) into the discriminator to determine the input veracity in the spatial and spectral domains. Besides, semantic restricted sampling is employed in SFGAN for synthesizing the image subject to a semantic mask. Further, the created samples are classified by the T-CNN-LSTM for predicting the VA scores of sentences. Finally, the experimental results exhibit that the A-T-CNN-LSTM on stanford sentiment Treebank (SST) and CIFAR-10 databases achieves 90.12% and 91% accuracy than the other tree-structured CNNs.