README.md

metadata

title: Tweets Sentiment Analyzer
emoji: 🚀
colorFrom: indigo
colorTo: gray
sdk: gradio
sdk_version: 5.34.2
app_file: app.py
pinned: false
license: mit
short_description: Real-Time Tweet Sentiment Analyzer

🧠 Sentiment Analysis from Scratch (BiLSTM + Attention)

Welcome to this live interactive demo of a sentiment analysis model trained completely from scratch using a Deep Bidirectional LSTM architecture enhanced with a custom attention mechanism. This project is designed to classify short texts or tweets into Positive or Negative sentiments with a confidence score.

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📌 Project Highlights

• ✅ Trained from scratch: The embedding layer is trained on the dataset itself (not using pretrained embeddings).
• 🧠 Model Architecture:
  • Bidirectional LSTM layers
  • Custom attention layer (BetterAttention)
  • Final dense ANN for binary classification
• 📊 Output: Label (Positive/Negative) and confidence score (0–1)
• 🔠 Tokenizer: Also trained from scratch and saved as tokenizer.joblib
• 📁 Model Format: Saved as .keras and loaded efficiently during inference

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🚀 Try it Out

Enter a tweet or short sentence below and see real-time prediction:

👉 Example:
"I absolutely loved the performance!"
Output: Positive (0.91)

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🛠 Model Files

You can also explore/download the trained artifacts here:

• sentiment_model.keras
• tokenizer.joblib

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🧪 How It Works

1. The input text is tokenized using the trained tokenizer (joblib).
2. The padded sequence is passed through:
   • Embedding → BiLSTM → BiLSTM → Attention → Dense Layers
3. The final sigmoid-activated output represents the probability of positivity.
4. A confidence-aware label is returned using Gradio’s Label component.

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📈 Model Performance

Despite training from scratch without pretrained embeddings (like GloVe or FastText), the model performs comparably well. Experiments with glove.27B.200d embeddings yielded similar accuracy, and hence were excluded for clarity.

Training plots and confusion matrix are available in the original Kaggle Notebook.

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🧑‍💻 Author

Shomya Soneji
Machine Learning & Deep Learning Enthusiast
Connect on Kaggle

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🤝 Support

If you find this project helpful, please consider giving it a 🌟 and sharing it!
Your feedback and suggestions are always welcome 💬