Benchmarking Open-Source Large Language Models for Sentiment and Emotion Classification in Indonesian Tweets

We benchmark 22 open-source large language models (LLMs) against ChatGPT-4 and human annotators on two NLP tasks—sentiment analysis and emotion classification—for Indonesian tweets. This study contributes to NLP in a relatively low-resource language (Bahasa Indonesia) by evaluating zer...

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主要な著者:	Arbi Haza Nasution, Aytug Onan, Yohei Murakami, Winda Monika, Anggi Hanafiah
フォーマット:	Artigo
言語:	Inglês
出版事項:	IEEE 2025-01-01
シリーズ:	IEEE Access
主題:	Annotation quality emotion classification sentiment analysis Indonesian language processing language models low-resource languages
オンライン･アクセス:	https://ieeexplore.ieee.org/document/11016677/
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