metadata

title: Emotion Classifier (NLP)
emoji: 🧠
colorFrom: indigo
colorTo: pink
sdk: streamlit
sdk_version: 1.32.2
app_file: app/app.py
pinned: false

Emotion Classifier (NLP)

A simple NLP-based emotion classification app that uses a fine-tuned transformer model on the GoEmotions dataset to predict the emotion conveyed in a given sentence.

Project Structure

Emotion-Classifier-NLP/ ├── notebooks/ │ ├── 01_exploration.ipynb │ ├── 02_training.ipynb │ ├── 03_evaluation.ipynb │ └── 04_comparison.ipynb ├── src/ │ ├── data_loader.py │ ├── model.py │ ├── model_hartmann.py │ ├── model_custom.py │ ├── train.py │ └── evaluate.py ├── app/ │ └── app.py ├── outputs/ │ ├── model/ # Trained model │ ├── metrics/ # Evaluation metrics │ └── interpretations/ # Integrated gradients visualization plots ├── requirements.txt ├── README.md └── .gitignore

Features

Uses Hugging Face Transformers with a fine-tuned model
Classifies emotion from text across 28 GoEmotions labels
Streamlit frontend for interactive use
Displays model prediction probabilities
Shows sample integrated gradients visualizations per label (optional)

Running the App Locally

Make sure you have Streamlit and other dependencies installed:

pip install -r requirements.txt

Then start the app using:

streamlit run app/app.py

Running the App on a web browser

You can visit https://emotion-classifier-nlp.streamlit.app/ to run the app online

Example Output

Input: "I find this funny"
Output: Predicted Emotion: amusement
Shows prediction probabilities across all 28 classes

Notes

Pretrained model is saved in outputs/model/
Integrated Gradients plots should be saved under outputs/interpretations/ and named using the format: sample_{n}_{label}.png

Performance Metrics

Metric	Score
Accuracy	60.2%
Macro F1	48.3%
Weighted F1	59.6%

Confusion matrix

Model Comparison (Hartmann vs Custom Model)

Sample Sentence	Hartmann Prediction(s)	Custom Model Prediction(s)
I love spending time with my family.	joy, sadness, disgust	love, joy, admiration
This is the worst day of my life.	disgust, anger, sadness	anger, surprise, disgust
I'm feeling very nervous about the exam.	fear, sadness, joy	nervousness, fear, embarrassment
What a beautiful sunset!	joy, surprise, neutral	admiration, excitement, joy
I feel so disappointed and frustrated.	sadness, anger, disgust	disappointment, annoyance, anger
I'm not sure how to feel about this.	neutral, disgust, sadness	confusion, optimism, disapproval
That was hilarious, I can't stop laughing!	joy, surprise, neutral	amusement, joy, optimism
I feel completely empty and lost.	sadness, neutral, disgust	surprise, disappointment, optimism

Insights:

The custom model captured more nuanced emotions, while Hartmann’s model tended to favor high-level emotions.
Some variance due to differences in label granularity between the models.
The custom model showed stronger performance in emotions like admiration, amusement, and disappointment.

Known Limitations

Single-label restriction: While the data supports multi-label emotion classification, the model currently predicts only the highest probability emotion.
Low support for some classes: Emotions like grief and pride had low representation in the training data.
Data bias: Results reflect Reddit comment biases present in the GoEmotions dataset.

Confidence Threshold

A confidence threshold of 0.6 is applied in the app.
If the top emotion’s probability is below this value, the app returns:

"Unclear / Not enough signal"

This prevents overconfident predictions on uncertain or ambiguous text.

Future Work

Expand to multi-label predictions to better capture complex emotions.
Improve minority class performance via data augmentation or rebalancing.
Incorporate explainability methods directly into the Streamlit app.
Deploy the app to Streamlit Cloud or Hugging Face Spaces.
Collect user feedback for real-world validation.

Credits

Model architecture: RoBERTa (Hugging Face)
Training dataset: GoEmotions
Reference model: Hartmann et al. (2023)
Streamlit App Framework: Streamlit
Berta Emotion Model: BERTa
Transformers Library: Hugging Face Transformers

License

This project is licensed under the MIT License.
You can use, modify, and distribute the software freely, but there is no warranty.