Emotion Prediction in Arabic Text

This repository contains the implementation of an emotion analysis model that predicts emotions from Arabic text. It leverages advanced machine learning techniques and NLP models like Arabert, Marabert, Gemini, and ChatGPT.

Features

Preprocessing of Arabic text data.
Use of TF-IDF, BM-25, and Word2Vec for text vectorization.
Integration of transfer learning models Arabert and Marabert.
Emotion prediction using machine learning algorithms including KNN, SVM, and Random Forest.
Utilization of generative AI models, such as Gemini and ChatGPT, to enhance emotion prediction capabilities.

Getting Started

Prerequisites

Python 3.x
pandas
numpy
scikit-learn
matplotlib
seaborn
nltk
regex
pyarabic
emoji
arabicstopwords

Installation

Clone the repository to your local machine:

git clone https://github.com/your-repo/emotion-prediction.git cd emotion-prediction

Install the required packages:

pip install -r requirements.txt

Usage

Load the data and run the main script:

import pandas as pd

Load your data

df = pd.read_csv('your-data.csv')

Assuming you have a function setup to preprocess and predict

from your_module import preprocess_data, predict_emotions

Preprocess data

preprocessed_data = preprocess_data(df)

Predict emotions

predicted_emotions = predict_emotions(preprocessed_data)

Models and Performance

Word Representation and Algorithms Performance

Word Representation	Algorithm	Accuracy	Precision (weighted avg)	Recall (weighted avg)	f1-score (weighted avg)
TF-IDF	KNN	0.88	0.89	0.88	0.88
	Random Forest	0.88	0.89	0.88	0.88
	SVM	0.89	0.90	0.89	0.89
BM-25	KNN	0.81	0.84	0.81	0.82
	Random Forest	0.82	0.83	0.82	0.82
	SVM	0.70	0.77	0.70	0.70
Word2Vec	KNN	0.69	0.73	0.69	0.71
	Random Forest	0.77	0.79	0.77	0.78
	SVM	0.50	0.57	0.50	0.49

Transfer Learning Model Performance

Model	Training Loss	Validation Loss	Accuracy	Roc Auc	f1-score (weighted avg)
UBC-NLP/MARBERTv2	0.08	0.25	0.66	0.90	0.83
CAMeL-Lab/bert-base-arabic-camelbert-mix	0.09	0.24	0.64	0.89	0.82
aubmindlab/bert-base-arabertv2	0.14	0.25	0.61	0.87	0.80
Bert After Translation	0.09	0.23	0.59	0.87	0.79

Generative AI Performance

Model	Accuracy	ROC AUC	Precision	Recall	f1-score (weighted avg)
ChatGPT 3.5-turbo	0.66	0.79	0.76	0.66	0.68
Gemini 1.5 Pro	0.67	0.79	0.73	0.67	0.69

Contributing

Contributions to this project are welcome! Please fork the repository and submit pull requests with your proposed changes.

License

Distributed under the MIT License. See LICENSE for more information.

Acknowledgments

Arabert and Marabert teams for providing pre-trained models.
OpenAI for the ChatGPT model.
Google for the Gemini model.

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