The Effectiveness of GPT-4 as Financial News Annotator Versus Human Annotator in Improving the Accuracy and Performance of Sentiment Analysis

Artificial intelligence (AI) has revolutionized various industries and has become a crucial component in the development of intelligent systems. One of the main challenges in the development of AI systems is data annotation, which involves labeling unlabelled data sets to train AI algorithms and Systems. The traditional manual data annotation method is time-consuming and requires skilled human annotators, which makes it expensive. Therefore, there is a need for effective and efficient data annotation tools. Recently, Generative Pre-Trained Transformer (GPT) models have shown remarkable success in various natural language processing tasks. GPT-4 is the latest version of these models and is expected to be a game-changer in data annotation tasks. This research aims to evaluate the effectiveness of GPT-4 as a data annotation tool versus traditional methods in improving the accuracy and performance of AI systems. The study will utilize a comparative experimental design in which both GPT-4 and traditional methods will be applied to the same data set, and the accuracy and performance of the AI system will be measured. In this paper, I evaluate how high-quality data annotation can help machine learning models make more accurate and reliable Sentiment Predictions. The findings of this research will provide insights into the feasibility and effectiveness of GPT-4 as a data annotation tool for the implementation of AI systems. If GPT-4 is proven to be more effective, it will revolutionize the data annotation industry. It will reduce the time and cost required and will open doors to more comprehensive data annotation tasks.