Arabic Sentiment Analysis

This project focuses on developing a sentiment analysis model for Arabic text, leveraging hybrid transformer-based models (AraBERT) and LSTM approaches. It aims to analyze and classify Arabic text into positive, negative, or neutral sentiment.

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🚀 Project Overview

Arabic sentiment analysis presents unique challenges due to the language's complexity and lack of annotated data. This project explores state-of-the-art techniques to overcome these challenges and improve sentiment classification accuracy.

Key Features:

• Transformer-Based Model: Integrated AraBERT for effective contextual representation.
• Hybrid Approach: Combined transformer outputs with an LSTM for improved performance.
• Comprehensive Review: Included a literature review of recent advancements in Arabic NLP.

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🛠️ Technologies Used

• Programming Languages: Python
• Libraries/Frameworks: PyTorch, Hugging Face Transformers, NumPy, Pandas
• Models: AraBERT, LSTM

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📊 Results

Model Performance:

• Accuracy: To be updated.
• F1-Score: To be updated.

📜 License

This project is licensed under the MIT License - see the LICENSE file for details.