The suggested emotion classification model yielded an 83% F1-score and an accuracy of 82.7% (Epoch=10, Batch Size=32)
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Plain Text:
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.
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BibTex:
@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}}
The authors express their gratitude to all internal and external sources that consistently provide support for our study, especially to Wilson Wongso, Steven Limcorn, and Cahya Wirawan for their openly usable models on HuggingFace.
