Using Text Data, Neural Networks are Trained to Develop Emotions that Mimic Human Emotional Understanding

Abstract

Recent methods of AI have revolutionized the field of computer science. Different sub-sectors of artificial intelligence (AI), like natural language processing (NLP) models, generative AI, computer vision, autonomous and recommendation systems, cybersecurity, quantum computing, etc., have helped automate human tasks, resulting in a tremendous amount of time and energy being saved. Despite the massive development of AI, all AI models lack one major factor, which is emotion. How can emotion be built into AI in order to for it to develop the emotional intelligence of the human brain to interpret and understand emotions so that it could create more human-friendly interactions? In this paper, we hypothesized developing emotions in neural networks as predictive sentiment analysis models using text data in order to replicate the emotional intelligence of the human brain to benefit human relationships. By using the Anaconda Repository, NVIDIA’s CUDA Toolkit, and TensorFlow, we were able to create a sentiment prediction model that achieved an accuracy of 94% and predicted the six basic emotions of joy, sadness, anger, fear, love, and surprise. Concluding this research, we observed that neural networks can develop the habit of recognizing emotions. This can be further fed into complex AI algorithms and systems to fine-tune emotional intelligence, resulting in more natural interactions, benefiting humans in