| Proj No. | A1059-251 |
| Title | LLM-Based Sentiment Analysis |
| Summary | Sentiment analysis is a crucial task in natural language processing (NLP) . Traditional approaches rely on rule-based or machine learning models that often struggle with nuanced language, contextual understanding, and domain adaptation. Large Language Models (LLMs) offer a promising solution by leveraging vast amounts of pre-trained knowledge to improve sentiment classification. This project aims to develop an LLM-based sentiment analysis system that enhances text understanding. |
| Supervisor | A/P Mao Kezhi (Loc:S2 > S2 B2C > S2 B2C 84, Ext: +65 67904284) |
| Co-Supervisor | - |
| RI Co-Supervisor | - |
| Lab | Internet of Things Laboratory (Loc: S1-B4c-14, ext: 5470/5475) |
| Single/Group: | Single |
| Area: | Digital Media Processing and Computer Engineering |
| ISP/RI/SMP/SCP?: |