Skip to content

Latest commit

 

History

History
33 lines (21 loc) · 1.2 KB

README.md

File metadata and controls

33 lines (21 loc) · 1.2 KB

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.

🚀 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.

🛠️ Technologies Used

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

📊 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.