| --- |
| license: mit |
| language: |
| - ar |
| - en |
| library_name: keras |
| --- |
| # Arabic Semantic / Sentiment Classification using BiLSTM |
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|
| This repository contains a TensorFlow/Keras-based **Bidirectional LSTM (BiLSTM)** model for Arabic text classification. |
| The model is designed for **binary classification tasks** such as sentiment or semantic polarity detection. |
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|
| ## Overview |
| - **Language:** Arabic |
| - **Task:** Binary text classification |
| - **Model:** BiLSTM neural network |
| - **Framework:** TensorFlow / Keras |
| - **Focus:** Emoji-aware preprocessing and Arabic stemming |
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|
| This project combines classical NLP preprocessing with deep learning to handle informal Arabic text, including emojis. |
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|
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| ## Model Architecture |
| The neural network architecture consists of: |
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|
| - Embedding layer (vocabulary size = 10,000, embedding dim = 128) |
| - Bidirectional LSTM (128 units, return sequences) |
| - Dropout (0.5) |
| - Bidirectional LSTM (64 units) |
| - Dense layer (32 units, ReLU) |
| - Output layer (1 unit, Sigmoid) |
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|
| Loss function: **Binary Crossentropy** |
| Optimizer: **Adam (lr = 0.001)** |
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|
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| ## Preprocessing Pipeline |
| The preprocessing steps are critical and must be applied **exactly as during training**: |
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|
| 1. Emoji conversion using `demoji` |
| 2. Whitespace and regex normalization |
| 3. Tokenization using NLTK |
| 4. Arabic stemming using **ISRIStemmer** |
| 5. Keras tokenization and padding (max length = 100) |
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|
| This pipeline allows the model to better handle: |
| - Informal Arabic |
| - Social media text |
| - Emoji-heavy content |
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|
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| ## Files in This Repository |
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|
| | File | Description | |
| |-----|------------| |
| | `lstm_text_model.h5` | Trained BiLSTM model | |
| | `tokenizer.pkl` | Keras tokenizer (must match training) | |
| | `label_encoder.pkl` | Label encoder for output mapping | |
| | `requirements.txt` | Python dependencies | |
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