AraBERT for Arabic Sentiment Analysis
Fine-tuned aubmindlab/bert-base-arabertv02 for Arabic sentiment classification (positive/negative).
🎯 Model Description
This model classifies Arabic text into positive or negative sentiment with 92.87% accuracy. Built using transfer learning on BERT-base-arabertv02 with task-specific fine-tuning for binary sentiment analysis.
🚀 Quick Start
from transformers import pipeline
# Load model
classifier = pipeline("sentiment-analysis", model="Belall87/arabert-arabic-sentiment")
# Predict
result = classifier("هذا المنتج رائع جداً")
print(result)
# [{'label': 'POSITIVE', 'score': 0.95}]
# Batch prediction
texts = [
"الخدمة ممتازة والموظفين متعاونين",
"تجربة سيئة جداً ولا أنصح بها"
]
results = classifier(texts)
📊 Performance
| Metric | Value |
|---|---|
| Accuracy | 92.87% |
| F1-Score | 92.87% |
| Precision | 92.85% |
| Recall | 92.89% |
🔧 Training Details
Base Model
- Architecture: BERT-base (12 layers, 768 hidden, 12 attention heads)
- Pre-trained: aubmindlab/bert-base-arabertv02
- Parameters: ~110M
Training
- Optimizer: AdamW
- Learning Rate: 2e-5 with cosine scheduling
- Batch Size: 8
- Epochs: 3
- Max Length: 256 tokens
- Hardware: GPU (fp16 mixed precision)
Data Preprocessing
- Arabic text normalization (alef, yeh, hamza variants)
- Diacritics removal
- URL, mention, hashtag filtering
- Stratified train/val/test split (72%/8%/20%)
💡 Intended Use
Direct Use
- Arabic social media sentiment monitoring
- Product review analysis
- Customer feedback classification
- Opinion mining in Arabic text
Limitations
- Binary classification only (positive/negative)
- Trained on Modern Standard Arabic (MSA) and dialectal mix
- May underperform on domain-specific jargon
- Best with text length < 256 tokens
📚 Training Data
Arabic sentiment dataset with stratified sampling for balanced training. Preprocessing includes normalization of Arabic orthographic variants and removal of noise.
🔗 Links
- GitHub Repository: Arabic-Sentiment-Analysis-BiLSTM-vs-AraBERT
- Kaggle Notebook: Arabic Sentiment Analysis
- Base Model: bert-base-arabertv02
📝 Citation
@misc{belal-arabert-sentiment-2025,
author = {Belal Mahmoud Hussien},
title = {AraBERT for Arabic Sentiment Analysis},
year = {2025},
publisher = {Hugging Face},
howpublished = {\url{https://huggingface.co/Belall87/arabert-arabic-sentiment}}
}
📧 Contact
Belal Mahmoud Hussien
- Email: belalmahmoud8787@gmail.com
- LinkedIn: Belal Mahmoud
- GitHub: @Bolaal
📄 License
MIT License