EmoSense-ID-Indonesian-Emotion-Classifier

Model Details

Model Description

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The EmoSense-ID is a model designed to identify and analyze emotions in Indonesian texts based on Plutchik's eight basic emotions: Anticipation, Anger, Disgust, Fear, Joy, Sadness, Surprise, and Trust. This model is developed using the NusaBERT-base and trained using Indonesian tweets categorized into eight emotion categories. The evaluation results of this model can be utilized to analyze emotions in social media, providing insights into users' emotional responses.

Bias

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Keep in mind that this model is trained using certain data which may cause bias in the emotion classification process. Therefore, it is important to consider and account for such biases when using this model.

Evaluation Results

The model was trained using the Hyperparameter Tuning technique with Optuna. In this process, Optuna conducted five trials to determine the optimal combination of learning rate (1e-6 to 1e-4) and weight decay (1e-6 to 1e-2). Each trial trained the BERT model with different hyperparameter configurations on the training dataset and then evaluated using the validation dataset. After all the experiments are completed, the best hyperparameter combination is used to train the final model. 

<table style="text-align: center; width: 100%;"> <tr> <th>Epoch</th> <th>Training Loss</th> <th>Validation Loss</th> <th>Accuracy</th> <th>F1</th> <th>Precision</th> <th>Recall</th> </tr> <tr> <td>1</td> <td>0.758400</td> <td>0.583508</td> <td>0.829932</td> <td>0.830203</td> <td>0.833136</td> <td>0.829932</td> </tr> <tr> <td>2</td> <td>0.370100</td> <td>0.394630</td> <td>0.866213</td> <td>0.865496</td> <td>0.870364</td> <td>0.866213</td> </tr> <tr> <td>3</td> <td>0.231500</td> <td>0.355294</td> <td>0.884354</td> <td>0.884585</td> <td>0.888140</td> <td>0.884354</td> </tr> <tr> <td>4</td> <td>0.071000</td> <td>0.322376</td> <td>0.902494</td> <td>0.902801</td> <td>0.904842</td> <td>0.902494</td> </tr> <tr> <td>5</td> <td>0.129900</td> <td>0.308596</td> <td>0.900227</td> <td>0.900340</td> <td>0.902132</td> <td>0.900227</td> </tr> </table>

Citation

@misc{Ardiyanto_Mikhael_2024,
    author    = {Mikhael Ardiyanto},
    title     = {EmoSense-ID},
    year      = {2024},
    URL       = {Aardiiiiy/EmoSense-ID-Indonesian-Emotion-Classifier},
    publisher = {Hugging Face}
}