Training neural networks on text data to model human emotional understanding

Recent advancements in artificial intelligence (AI) have revolutionized the field of computer science. Different subsectors of 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 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 work, we hypothesized that training neural networks to predict emotions using text-based sentiment analysis will lead to significant improvements in AI’s abilities to classify emotional states. By using NVIDIA 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 the future.