🧠 EmotionSense

Fine-Grained Emotion Classification using RoBERTa

🤗 Hugging Face • 🔥 RoBERTa • 💬 Emotion AI • 🚀 Production Ready

EmotionSense is a RoBERTa-base model fine-tuned for emotion classification. The model predicts seven human emotions from English text using a cleaned and simplified version of the GoEmotions dataset.

Unlike the original GoEmotions dataset containing 28 fine-grained emotion labels, this work reorganizes emotions according to the Ekman Emotion Framework, improving interpretability while maintaining strong predictive performance.

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✨ Highlights

• 🔥 Fine-tuned RoBERTa-base
• 🧠 Context-aware emotion recognition
• 📊 Class-balanced training using Weighted Cross Entropy Loss
• ⚡ Early stopping for improved generalization
• 🎯 Optimized using Weighted F1 Score
• 🤗 Compatible with Hugging Face Transformers Pipeline

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🎯 Supported Emotion Classes

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📚 Dataset

The model was trained using a customized version of the GoEmotions dataset.

The original dataset contains approximately 58,000 Reddit comments annotated with 28 fine-grained emotion labels.

To improve annotation quality:

• ✔ Majority Voting was applied.
• ✔ Samples without annotator agreement were removed.
• ✔ Multi-label ambiguity was eliminated.
• ✔ Fine-grained emotions were mapped into Ekman's seven universal emotion categories.

Emotion Mapping

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🏗 Model Architecture

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📈 Performance

EmotionSense achieved the best performance among all evaluated models.

Final Evaluation

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🚀 Quick Start

Install Transformers

pip install transformers torch

Load the model

from transformers import pipeline

classifier = pipeline(
    "text-classification",
    model="SobanHM/EmotionSense"
)

classifier("I finally got my dream job today!")

Example Output

[
    {
        "label": "joy",
        "score": 0.997
    }
]

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💡 Applications

• Conversational AI
• Emotion-aware Chatbots
• Mental Health Support Systems
• Customer Feedback Analysis
• Social Media Analytics
• Human-Computer Interaction
• Intelligent Virtual Assistants

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⚠ Limitations

• Supports English language only.
• Performance may decrease on domain-specific text.
• Sarcasm and irony remain challenging.
• Emotion recognition is probabilistic and should not be used for clinical diagnosis or psychological assessment.

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👨‍💻 About the Author

Soban Hussain

AI Engineer • Machine Learning Researcher • Computer Vision & NLP

🤗 Hugging Face: https://huggingface.co/SobanHM

💼 LinkedIn: https://www.linkedin.com/in/sobanhussain

💻 GitHub: https://github.com/SobanHM

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🙌 Acknowledgements

This project was built using:

• Hugging Face Transformers
• PyTorch
• GoEmotions Dataset
• RoBERTa

Special thanks to the open-source AI community for providing exceptional tools and resources.

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📖 Citation

If you use this model in your research, please cite both this repository and the GoEmotions dataset.

@inproceedings{demszky2020goemotions,
  title={GoEmotions: A Dataset of Fine-Grained Emotions},
  author={Demszky, Dorottya and others},
  booktitle={Proceedings of ACL},
  year={2020}
}

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