| --- |
| 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) |
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| 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 |
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| - ✅ **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 |
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| Enter a tweet or short sentence below and see real-time prediction: |
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| 👉 *Example*: |
| `"I absolutely loved the performance!"` |
| **Output**: Positive (0.91) |
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| --- |
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| ## 🛠 Model Files |
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| You can also explore/download the trained artifacts here: |
| - [`sentiment_model.keras`](https://huggingface.co/MasterShomya/Sentiment_Analysis-Tweets/blob/main/sentiment_model.keras) |
| - [`tokenizer.joblib`](https://huggingface.co/MasterShomya/Sentiment_Analysis-Tweets/blob/main/tokenizer.joblib) |
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| --- |
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| ## 🧪 How It Works |
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| 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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| --- |
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| ## 📈 Model Performance |
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| 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. |
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| Training plots and confusion matrix are available in the original [Kaggle Notebook](https://www.kaggle.com/code/mastershomya/sentiment-analysis-deep-bilstm). |
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| --- |
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| ## 🧑💻 Author |
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| **Shomya Soneji** |
| Machine Learning & Deep Learning Enthusiast |
| Connect on [Kaggle](https://www.kaggle.com/mastershomya) |
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| ## 🤝 Support |
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| If you find this project helpful, please consider giving it a 🌟 and sharing it! |
| Your feedback and suggestions are always welcome 💬 |
