Fine-Tuning BERT for Sentiment Analysis: Arabic Reviews 🚀

This project provides a comprehensive guide to fine-tuning BERT for sentiment analysis on Arabic reviews using Google Colab. We'll leverage the power of BERT and Google Colab's GPU capabilities to build a custom sentiment classifier, specifically tailored for Arabic text.

Hugging Face Model 📦

You can access and use the fine-tuned model for sentiment analysis directly from Hugging Face: Arabic Sentiment BERT Model

Author 🧑‍💻

Khaled Soudy

Introduction 📖

Sentiment analysis is a crucial task in Natural Language Processing (NLP) that aims to determine the emotional tone behind a piece of text, such as positive 😄, negative 😠, or neutral 😐. BERT (Bidirectional Encoder Representations from Transformers) is a state-of-the-art language model that has achieved remarkable success in various NLP tasks, including sentiment analysis. We'll fine-tune a pre-trained BERT model using an Arabic reviews dataset to build a powerful sentiment classifier for this specific domain.

What’s the Task?

We’re working with the Arabic 100k Reviews dataset—a compilation of 100k (well, 99,999) Arabic reviews from hotels, books, movies, products, and airlines. Each review falls into one of three sentiment categories:
1️⃣ Positive 🌟
2️⃣ Negative 😠
3️⃣ Mixed 🤔

📂 About the Dataset:

• Content: Each review includes a sentiment label (Positive, Negative, or Mixed) and the review text, separated by tabs (TSV format).
• Processing: Reviews were cleaned of diacritics and non-Arabic characters, ensuring high-quality data.
• Balanced Classes: No duplicate reviews, making it a robust dataset for classification tasks.

Why Fine-Tune BERT?

BERT has a deep contextual understanding of text, but by fine-tuning it with our dataset, we’ll train it to recognize sentiment in Arabic reviews with precision.

What to Expect?

1️⃣ Load and explore the dataset to understand what we’re working with.
2️⃣ Prepare BERT for sentiment analysis.
3️⃣ Train and evaluate our custom sentiment classifier.

Project Structure 📂

The project is organized as follows:

• Fine-Tuning BERT for Sentiment Analysis.ipynb: Google Colab Notebook containing the main code and explanations for fine-tuning BERT on Arabic reviews.
• ar_reviews_100k.tsv: Dataset containing 100k Arabic reviews for sentiment analysis (Positive, Negative, Mixed).
• README.md: This file.

Getting Started 🏁

1. Open the Google Colab Notebook: Open the Fine-Tuning BERT for Sentiment Analysis.ipynb notebook in Google Colab.
2. Enable GPU: For faster training, it is highly recommended to enable GPU acceleration in Colab. Go to Runtime > Change runtime type and select GPU as the hardware accelerator.
3. Install Dependencies: Install the necessary libraries using the provided code cells in the notebook (transformers, ktrain, tensorflow).
4. Mount Google Drive: Mount your Google Drive to access the dataset (ar_reviews_100k.tsv) stored in your Drive.
5. Run the Code: Execute the code cells in the notebook sequentially to understand the steps involved in fine-tuning BERT for sentiment analysis on Arabic reviews.
   • This includes loading the dataset, preprocessing, training, validating, and saving the fine-tuned model.
6. Explore the Dataset: Examine the ar_reviews_100k.tsv file to understand the data used for sentiment analysis.
   • This dataset contains 100,000 Arabic reviews labeled with sentiment categories: Positive, Negative, or Mixed.

Key Features ✨

• Fine-tuning BERT: This project focuses on fine-tuning a pre-trained BERT model, specifically adapting it for sentiment analysis in Arabic.
• Arabic Sentiment Analysis: We'll leverage a large dataset of Arabic reviews to train a custom sentiment classifier.
• Google Colab Integration: The project is designed to be run seamlessly within Google Colab, taking advantage of its GPU capabilities.
• Step-by-Step Guide: The Google Colab Notebook provides a detailed walkthrough of the process, making it accessible for beginners.
• Evaluation and Saving: We'll evaluate the model's performance and save the fine-tuned model for later use.

Conclusion 🎉

This project equips you with the knowledge and tools to perform sentiment analysis on Arabic text using fine-tuned BERT in Google Colab. By following the steps in the notebook, you can build a powerful sentiment classifier for your specific needs.

License 📜

This project is licensed under the MIT License.