IndoRoBERTa-Base-Sentiment-Indonesian-Social-Media

Evaluation

The accuracy and F1-score values for the proposed sentiment classification model resulted in 98% accuracy and 97.4% F1-score (Epoch=10, Batch Size=32).

Citation

• 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.

• 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}}

Acknowledgment

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.