Indoroberta Base Sentiment Indonesian Social Media

🚀 情感分類模型

本項目聚焦於情感分類模型，通過優化模型實現了高準確率和F1分數，為情感分析領域提供了有效的解決方案。

🚀 快速開始

如需參考模型卡片元數據，請查看規範：https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1 文檔/指南：https://huggingface.co/docs/hub/model-cards

📚 詳細文檔

評估

所提出的情感分類模型的準確率和F1分數達到了98%的準確率和97.4%的F1分數（Epoch = 10，批量大小 = 32）。

引用

• 純文本引用： Y. O. Sihombing, R. Fuad Rachmadi, S. Sumpeno and M. J. Mubarok, "Optimizing IndoRoBERTa Model for Multi-Class Classification of Sentiment & Emotion on Indonesian Twitter," 2024 IEEE 10th Information Technology International Seminar (ITIS), Surabaya, Indonesia, 2024, pp. 12 - 17, doi: 10.1109/ITIS64716.2024.10845566.
• BibTeX引用：

@INPROCEEDINGS{10845566,
  author={Sihombing, Yogie Oktavianus and Fuad Rachmadi, Reza and Sumpeno, Surya and Mubarok, Moh. Jabir},
  booktitle={2024 IEEE 10th Information Technology International Seminar (ITIS)}, 
  title={Optimizing IndoRoBERTa Model for Multi-Class Classification of Sentiment & Emotion on Indonesian Twitter}, 
  year={2024},
  volume={},
  number={},
  pages={12-17},
  keywords={Seminars;Sentiment analysis;Accuracy;Social networking (online);Blogs;Text categorization;Predictive models;Emotional responses;Information technology;Optimization;IndoRoBERTa;classification;sentiment;emotion;Indonesian;Twitter},
  doi={10.1109/ITIS64716.2024.10845566}
}

致謝

作者對所有持續支持本研究的內外部資源表示感謝，尤其感謝Wilson Wongso、Steven Limcorn和Cahya Wirawan在HuggingFace上公開可用的模型。