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Using NLP principles we need to build a model that analysis Arabic tweets that related to COVID-19

Smart Sentiment Analysis and Classification of Arabic Tweets by building and merging deep learning models

Training multilingual models for sentiment analysis: by fine-tuning pre-trained multilingual language models such as multiBERT, XLMRoberta, etc. on the sentiment prediction task and comparing the performance of such multilingual models with (1) monolingual English model trained on English tweets and test on Arabic tweets translated to English (with pre-trained machine translation or Google Translate) and (2) multilingual model trained on English tweets and test on Arabic tweets (so, zero-shot classification). Do multilingual models benefit from being trained on multilingual data?

Graduation project: Streamlit app comparing Arabic BERT and Logistic Regression for sentiment classification of Saudi banks’ tweets using a shared train/validation/test split.

Fine-tuning of CAMeLBERT-Mix for Arabic dialect sentiment analysis on ArSarcasm (~10K tweets). Macro-F1 = 0.74 on 3-class classification (positive/negative/neutral). PyTorch · HuggingFace Transformers · pyarabic.

A Multilingual Sentiment Analysis project using XML-RoBERTa. This model analyzes sentiment in 8 languages (Arabic, English, French, German, Hindi, Italian, Spanish, and Portuguese). If an input tweet is in an unsupported language, it is first translated to English before sentiment classification.

A machine learning project for classifying Arabic tweets into positive or negative sentiments using natural language processing (NLP) techniques. The system preprocesses Arabic text, extracts features using TF-IDF, and trains a Logistic Regression model, achieving high accuracy while addressing the challenges of Arabic morphology.

Sentiment classification of Arabic tweets using the Tensorflow framework

Models for event detection, summarization, topic classification and sentiment analysis of arabic tweets

CS 410: Text Information Systems Project to implement a Classification Model for Sentiment Analysis on Arabic Tweets

This project performs sentiment analysis on Arabic tweets using lexicon-based and machine learning approaches. It involves data preprocessing, sentiment classification, and comparison of techniques based on accuracy, precision, recall, and F1-score. The project also explores predictive modeling to forecast sentiment trends.