Sim, Kah Hoe (2024) Accelerated personalized stock sentiment analysis: Leveraging LLMS for Youtuber content and news articles. Final Year Project, UTAR.
Abstract
This project examines the transformative potential of natural language processing (NLP), specifically through the use of ChatGPT, in the realm of stock investment. The primary goal is to create a dynamic, user-focused AI-driven system that provides investors with real-time insights, tailored analyses, and enhanced decision support for the stock market. The project encompasses a broad scope, including data integration, model adaptation, system development, performance evaluation, and ongoing improvements. Central to this effort is the use of ChatGPT 4.0. This interdisciplinary approach highlights the project's dedication to bridging the gap between AI and stock investment. The project's innovation lies in its ability to enhance decision-making support for investors by leveraging AI's NLP capabilities to facilitate intuitive interactions and deliver real-time insights. The iterative learning process ensures that the system remains adaptable and continuously improves, while comprehensive documentation aids in knowledge sharing within the financial sector. In essence, this research represents a significant advancement toward democratizing stock investment, making it more accessible and data-driven. By leveraging ChatGPT and cutting-edge technologies, the project provides investors with a valuable tool for navigating the complexities of the stock
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