LAUSR

Emotional intelligence plays an important role in human intelligence and is a recent research hotspot. With the rapid development of deep learning techniques in recent years, several neural network-based emotional text generation methods have been investigated. However, the existing emotional text generation approaches often suffer from the problem of requiring large-scale annotated data. Generative adversarial network (GAN) has shown promising results in natural language generation and data enhancement. In order to solve the above problem, this paper proposes a GAN-based cross-domain text sentiment transfer model, which uses annotated data from other domains to assist in the training of emotional text generation network. By combining adversarial reinforcement learning with supervised learning, our model is able to extract patterns of sentiment transformation and apply them in emotional text generation. The experimental results have shown that our approach outperforms the state-of-the-art methods and is able to generate high-quality emotional text while maintaining the consistency of domain information and content semantics.