hashF/arabic_semantics

Arabic Semantic / Sentiment Classification using BiLSTM

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.

Overview

• Language: Arabic
• Task: Binary text classification
• Model: BiLSTM neural network
• Framework: TensorFlow / Keras
• Focus: Emoji-aware preprocessing and Arabic stemming

This project combines classical NLP preprocessing with deep learning to handle informal Arabic text, including emojis.

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Model Architecture

The neural network architecture consists of:

• 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)

Loss function: Binary Crossentropy
Optimizer: Adam (lr = 0.001)

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Preprocessing Pipeline

The preprocessing steps are critical and must be applied exactly as during training:

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)

This pipeline allows the model to better handle:

• Informal Arabic
• Social media text
• Emoji-heavy content

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Files in This Repository

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