EGAN: Generatives Adversarial Networks for Text Generation with Sentiments

In these last years, communication with computers has made enormous steps, like the robot Sophia that surprised many people with their human interactions, behind this kind of robot, there is a machine learning model for text generation to interact with others, but in terms of text generation with sentiments not many investigations have been done. A model like GAN has opportunities to become an excellent option to attack this new problem because of their discriminator and generator competing for search the optimal solution. In this paper, a GAN model is presented that can generate text with different emotions based on a dataset recompiled from tweets labeled with emotions and then deployed in an NAO robot to speak the text in short phrases using voice commands. The model is evaluated with different methods popular in text generation like BLLEU and additionally, a human experiment is done to prove the quality and sentiment accuracy.