AI Insight
This study introduces MASHA (Multi-Agent System for Healthcare Sentiment Analysis), an AI-driven framework that combines multiple machine learning models, specifically Support Vector Machines, Naive Bayes, and Logistic Regression, through ensemble techniques to classify sentiment in Arabic tweets related to migraines. The system employs a multi-agent architecture to manage data acquisition, preprocessing, model training, and real-time decision-making. Tested on a dataset of Arabic migraine-related tweets, MASHA achieved an accuracy of 90.0% and an F1-score of 89.46%, outperforming traditional single-model approaches.
Why it matters
Automated sentiment analysis of social media content in Arabic could support real-time public health surveillance and provide healthcare providers with insights into patient experiences regarding migraine and potentially other conditions. The framework's described scalability suggests possible extension to other languages and health topics, though this remains to be validated.
⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
Migraine detection and sentiment analysis in healthcare have become increasingly important, particularly with the rise of social media platforms like Twitter, where users often share their personal health experiences. This study presents MASHA (Multi-Agent System for Healthcare Sentiment Analysis), an artificial intelligence (AI)-driven framework that integrates multiple machine learning (ML) models for sentiment analysis of Arabic tweets related to migraines. The system leverages a multi-agent architecture to handle tasks such as data acquisition, pre-processing, model training and real-time decision-making. Key ML models, including Support Vector Machines (SVM), Naive Bayes (NB) and Logistic Regression (LR), are integrated using ensemble techniques, leading to improved classification performance. Experiments conducted on a dataset of Arabic tweets demonstrate that MASHA outperforms traditional methods, achieving an accuracy of 90.0% and an F1-score of 89.46%. Moreover, the system’s scalability and flexibility make it suitable for real-time public health monitoring, offering valuable insights into patient experiences and public sentiment regarding healthcare services. MASHA’s adaptability suggests its potential application for analysing other healthcare-related conditions, reinforcing the system’s scalability and broader relevance. Future work will focus on incorporating deep learning (DL) models and expanding the dataset with content from additional social media platform.