README.md

metadata

title: 🐦 TwittBERTO
emoji: 🚗
colorFrom: blue
colorTo: indigo
sdk: streamlit
sdk_version: 1.42.2
app_file: app.py
pinned: false

This project demonstrates a sentiment analysis pipeline built with DistilBERT, a lightweight transformer model developed by Hugging Face. The model was fine-tuned on a dataset of 16,000 tweets to classify sentiment into categories such as Positive, Negative, and Neutral. The final model achieved an impressive 90% accuracy on the validation set.

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🚀 Features

• Utilizes DistilBERT for high-performance NLP with lower resource consumption.
• Cleaned and preprocessed Twitter data (16K rows).
• Fine-tuned with PyTorch and Hugging Face Transformers.
• Achieved 90%+ accuracy on sentiment classification.
• Includes training, validation, and evaluation pipelines.

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📁 Dataset

• 16,000 manually labeled tweets with three sentiment classes:

  • Positive
  • Negative
  • Neutral

• Dataset was preprocessed to remove mentions, hashtags, links, and special characters.

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🧠 Model

• Base Model: distilbert-base-uncased
• Fine-tuning: Trained for several epochs using a cross-entropy loss function and AdamW optimizer.
• Tokenizer: Hugging Face DistilBertTokenizerFast
• Training Framework: PyTorch + Hugging Face Trainer API

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📊 Performance

Metric	Score
Accuracy	90%
Precision	High
Recall	High
F1-score	High

Note: Actual precision, recall, and F1-score values can be added if available.

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📦 Dependencies

transformers==4.x.x
torch==1.x
scikit-learn
pandas
numpy
matplotlib

Install with:

pip install -r requirements.txt

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🛠️ How to Run

1. Clone the repository:

   git clone https://github.com/yourusername/twitter-sentiment-distilbert.git
   cd twitter-sentiment-distilbert
   

2. Install dependencies:

   pip install -r requirements.txt
   

3. Train the model:

   python train.py
   

4. Evaluate the model:

   python evaluate.py
   

5. Run prediction on new tweets:

   python predict.py --text "I love this app!"
   

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📈 Example Output

Input: "I love this app!"
Predicted Sentiment: Positive

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📚 Future Improvements

• Integrate with a live Twitter API for real-time sentiment tracking.
• Add a web dashboard using Streamlit or Flask.
• Extend to multilingual support using xlm-roberta.

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📄 License

This project is open-source and available under the MIT License.

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