| 作成者 |
|
|
|
|
|
|
|
| 本文言語 |
|
| 出版者 |
|
| 発行日 |
|
| 収録物名 |
|
| 巻 |
|
| 開始ページ |
|
| 終了ページ |
|
| 会議情報 |
|
| 出版タイプ |
|
| アクセス権 |
|
| 権利関係 |
|
| 権利関係 |
|
| 概要 |
Due to the informality of social media, Malay user-generated content sentiment analysis is difficult. Existing methods struggle to capture cultural and contextual details. This study proposes publishi...ng an open-source annotated dataset, fine-tuning an open-source large language model (LLM), and using an open-source chatbot interface to create a robust sentiment analysis model for noisy Malay text. The research addresses three main issues: lack of labelled Malay social media data, insufficient generic Malay language models, and lack of practical sentiment analysis tools. Its three goals are to create a diverse dataset with accurate sentiment labels, parameter-efficiently fine-tune an LLM, and export the model for an interactive chatbot. The process involves collecting social media data using Contextual Lexical Adaptation, preprocessing and analysing it, fine-tuning the TinyLlama LLM using LoRA, and comparing it to traditional models. Real-world applications, such as sentiment analysis of Malaysian tweets, will be shown using a locally deployed chatbot interface for fine-tuned model inference. This study lays the groundwork for practical sentiment analysis, benefiting businesses, researchers, and politicians seeking data-driven insights. This research aims to revolutionise open-source Malay sentiment analysis by addressing current limitations through an integrated approach.続きを見る
|
Mendeley出力
2025_p1904