Emotion Classification using Generative Pre-trained Embedding and Machine Learning

Emotion detection from user-generated text has drawn tremendous attention in the recent era for multiple reasons. Hence, there remain significant prospects in the urge for a state-of-the-art approach for emotion classification. In this research article, we explore the realm of GPT (Generative Pre-trained Transformer) models and their embedding, the extent of application of GPT text embedding for emotion analysis is still limited. In this paper, we explore the advantages of GPT embedding and focus on its integration. In this paper, we synthesized key findings and insights on different embedding techniques adopted for emotion prediction. Our research aims to contribute to the ongoing evolution of emotion classification techniques. We proposed an innovative approach to leverage GPT embedding acquisition step for fine-grained emotion classification from user-generated text using machine learning algorithms. Our results suggest that GPT embeddings can be effectively used to improve the accuracy and effectiveness of machine learning models for emotion detection and sentiment analysis tasks. This is likely due to the semantic strength of GPT embeddings and their ability to encode rich linguistic data. In this article, we discuss the potential breakthroughs offered by this novel approach for improved performance in emotion classification using machine learning algorithms.