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--- |
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title: Tweets Sentiment Analyzer |
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emoji: 🚀 |
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colorFrom: indigo |
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colorTo: gray |
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sdk: gradio |
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sdk_version: 5.34.2 |
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app_file: app.py |
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pinned: false |
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license: mit |
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short_description: Real-Time Tweet Sentiment Analyzer |
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--- |
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# 🧠 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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--- |
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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). |
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- 🧠 **Model Architecture**: |
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- Bidirectional LSTM layers |
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- Custom attention layer (`BetterAttention`) |
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- Final dense ANN for binary classification |
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- 📊 **Output**: Label (Positive/Negative) and confidence score (0–1) |
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- 🔠 **Tokenizer**: Also trained from scratch and saved as `tokenizer.joblib` |
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- 📁 **Model Format**: Saved as `.keras` and loaded efficiently during inference |
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--- |
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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*: |
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`"I absolutely loved the performance!"` |
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**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: |
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- [`sentiment_model.keras`](https://huggingface.co/MasterShomya/Sentiment_Analysis-Tweets/blob/main/sentiment_model.keras) |
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- [`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`). |
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2. The padded sequence is passed through: |
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- `Embedding → BiLSTM → BiLSTM → Attention → Dense Layers` |
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3. The final sigmoid-activated output represents the **probability of positivity**. |
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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** |
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Machine Learning & Deep Learning Enthusiast |
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Connect on [Kaggle](https://www.kaggle.com/mastershomya) |
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--- |
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## 🤝 Support |
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If you find this project helpful, please consider giving it a 🌟 and sharing it! |
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Your feedback and suggestions are always welcome 💬 |
