AI-powered Mathematical Sentiment Model and graph theory for social media trends
Year: 2024
Type: article
Abstract: Significant issues have arisen as a result of the global spread of monkeypox, such as the extensive transmission of false information, public fear, and stigmatization on social media. Increased fear, prejudice, stigmatization of minority groups, and opposition to public health initiatives are frequently the results of these problems. Furthermore, health authorities are unable to provide correct information and prompt actions due to a lack of efficient methods for analyzing the enormous amounts of unstructured social media data. This disparity weakens crisis management initiatives and increases public skepticism of health guidelines. In order to address these issues, this study looks into the attitude around monkeypox on social media in order to pinpoint public worries, counter false information, and enhance communication tactics. The study intends to improve public comprehension, offer practical insights, and help health authorities manage the outbreak by fusing graph theory with AI-driven sentiment analysis. In order to facilitate semantic analysis of tweets through structured information extraction, graph theory is used to organize unstructured or semi-structured data by creating meaningful links between entities. Furthermore, opinions on monkeypox infection in social media are analyzed and user sentiments are detected using a reinforcement Markov decision process. According to experimental results, the suggested model's accuracy on the Monkeypox tweet dataset was 98 %. These results help raise awareness of monkeypox among the general population and promote an educated and robust social response.
Authors: M. VENKATACHALAM, R․VIKRAMA PRASAD
Institution: Government of Tamil Nadu
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid:
OpenAlex ID: W4410457688