🧠 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

Label	Description
😀 Joy	Positive emotions, happiness, gratitude, love
😢 Sadness	Grief, disappointment, remorse
😡 Anger	Anger, annoyance, disapproval
😨 Fear	Fear and nervousness
🤢 Disgust	Disgust
😲 Surprise	Surprise, curiosity, realization
😐 Neutral	Emotionally neutral statements

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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

Ekman Category	Original GoEmotions Labels
Anger	anger, annoyance, disapproval
Disgust	disgust
Fear	fear, nervousness
Joy	joy, amusement, admiration, approval, caring, desire, excitement, gratitude, love, optimism, pride, relief
Sadness	sadness, disappointment, embarrassment, grief, remorse
Surprise	surprise, realization, curiosity, confusion
Neutral	neutral

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🏗 Model Architecture

Property	Value
Base Model	RoBERTa-base
Framework	Hugging Face Transformers
Language	English
Task	Emotion Classification
Max Sequence Length	128
Batch Size	16
Learning Rate	2e-5
Epochs	5
Optimizer	AdamW
Loss Function	Weighted Cross Entropy
Early Stopping	Enabled

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📈 Performance

EmotionSense achieved the best performance among all evaluated models.

Model	Accuracy	Weighted F1
Logistic Regression	0.59	0.57
Random Forest	0.61	0.59
Linear SVM	0.63	0.61
DistilBERT	0.68	0.67
EmotionSense (RoBERTa)	0.903	0.891

Final Evaluation

Metric	Score
Accuracy	90.3%
Precision	90.1%
Recall	89.4%
Weighted F1	89.1%

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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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