This model is a fine-tuned version of IndoBertweet-base-uncased for Indonesian sentiment analysis. The model is designed to classify sentiment into three categories: negative, positive, and neutral. It was trained on a diverse dataset comprising reactions from Twitter and other social media platforms, covering various topics, including politics, disasters, and education. The model is optimized using Optuna for hyperparameter tuning and evaluated using accuracy, F1-score, precision, and recall metrics.

Do consider that this model is trained using certain data, which may cause bias in the sentiment classification process. The model may inherit socio-cultural biases from its training data and may be less accurate for the most recent events that are not covered in the data. The limitation of the three categories may also not fully grasp the complexity of emotions, especially in capturing particular contexts. Therefore, it is important to consider and account for such biases when using this model.

The training process uses hyperparameter optimization techniques with Optuna. The model was trained for a maximum of 10 epochs with a batch size of 16, using an optimized learning rate and weight decay. The evaluation strategy is performed every 100 steps, saving the best model based on accuracy. The training also applied early stopping with patience 3 to prevent overfitting.

@misc{Ardiyanto_Mikhael_2024,
    author    = {Mikhael Ardiyanto},
    title     = {Aardiiiiy/indobertweet-base-Indonesian-sentiment-analysis},
    year      = {2024},
    URL       = {https://huggingface.co/Aardiiiiy/indobertweet-base-Indonesian-sentiment-analysis},
    publisher = {Hugging Face}
}