Accelerated personalized stock sentiment analysis: Leveraging LLMS for Youtuber content and news articles
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 anal...
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| Format: | Final Year Project / Dissertation / Thesis |
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2024
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| Online Access: | http://eprints.utar.edu.my/6996/ http://eprints.utar.edu.my/6996/1/fyp_CS_2024_SKH.pdf |
| _version_ | 1848886821354733568 |
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| author | Sim, Kah Hoe |
| author_facet | Sim, Kah Hoe |
| author_sort | Sim, Kah Hoe |
| building | UTAR Institutional Repository |
| collection | Online Access |
| description | 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 |
| first_indexed | 2025-11-15T19:44:35Z |
| format | Final Year Project / Dissertation / Thesis |
| id | utar-6996 |
| institution | Universiti Tunku Abdul Rahman |
| institution_category | Local University |
| last_indexed | 2025-11-15T19:44:35Z |
| publishDate | 2024 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | utar-69962025-02-27T07:16:32Z Accelerated personalized stock sentiment analysis: Leveraging LLMS for Youtuber content and news articles Sim, Kah Hoe T Technology (General) 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 2024-05 Final Year Project / Dissertation / Thesis NonPeerReviewed application/pdf http://eprints.utar.edu.my/6996/1/fyp_CS_2024_SKH.pdf Sim, Kah Hoe (2024) Accelerated personalized stock sentiment analysis: Leveraging LLMS for Youtuber content and news articles. Final Year Project, UTAR. http://eprints.utar.edu.my/6996/ |
| spellingShingle | T Technology (General) Sim, Kah Hoe Accelerated personalized stock sentiment analysis: Leveraging LLMS for Youtuber content and news articles |
| title | Accelerated personalized stock sentiment analysis: Leveraging LLMS for Youtuber content and news articles |
| title_full | Accelerated personalized stock sentiment analysis: Leveraging LLMS for Youtuber content and news articles |
| title_fullStr | Accelerated personalized stock sentiment analysis: Leveraging LLMS for Youtuber content and news articles |
| title_full_unstemmed | Accelerated personalized stock sentiment analysis: Leveraging LLMS for Youtuber content and news articles |
| title_short | Accelerated personalized stock sentiment analysis: Leveraging LLMS for Youtuber content and news articles |
| title_sort | accelerated personalized stock sentiment analysis: leveraging llms for youtuber content and news articles |
| topic | T Technology (General) |
| url | http://eprints.utar.edu.my/6996/ http://eprints.utar.edu.my/6996/1/fyp_CS_2024_SKH.pdf |