This project investigates how the Attention mechanism improves Arabic text classification. It compares deep learning models, LSTM and CNN, that lack Attention, with AraBert, a Transformer model that utilizes Attention.
You can find the full research report on this project here.
Datasets used in the project can be found here:
- CNN Arabic news dataset:
cnn-arabic-utf8(primary dataset) - BBC Arabic news dataset:
bbc-arabic-utf8
The following deep learning models were tested:
- LSTM Model: A Long short-term memory model for text classification.
- CNN Model: A convolutional neural network model for text classification.
- AraBERT Model: A transformer-based model specifically designed for Arabic text.
processing-files.ipynb: Create .csv dataset from the downloaded dataset.preprocessing.ipynb: Exploratory data analysis (EDA) and preprocessing steps, performed on the primary dataset CNN.data_augmentation.ipynb: Data augmentation techniques performed.arabic_text_classification_LSTM_CNN.ipynb: CNN and LSTM models implementation for Arabic text classification.text_classification_with_AraBert.ipynb: AraBERT model implementation for Arabic text classification.arabic-sentiment-analysis-lstm-cnn (1).ipynb: Arabic tweets sentiment analysis using LSTM and CNN.AraBERT_text_classification_and_sentiment_analysis.ipynb: AraBERT model implementation for Arabic text classification + Arabic tweets sentiment analysis.
dataset.csv: The .csv dataset created from original .txt files.bbc-arabic-utf8_folder_form.rar: Primary dataset: BBC Arabic news in a folder structure. Each folder corresponds to a specific category and contains .txt files related to that category.cnn-arabic-utf8_csv_form.zip: CNN dataset in .csv format.train_data.csv: Dataset after performing Data Augmentation.
This project is licensed under the MIT License - see the LICENSE.md file for details.
- The datasets used were sourced from Arabic Corpora on SourceForge.
For any inquiries or contributions, please contact [[email protected]].