Applying Artificial Intelligence to Social Media Sentiment Analysis for Immediate Crisis Response

Abstract

Social media's ability to spread information swiftly during emergencies has pros and cons for real-time crisis response. This article examines sentiment analysis using AI to better social media crisis management. Using machine learning and NLP algorithms, artificial intelligence (AI) can analyze massive social media data, estimate public sentiment, identify new issues, and test people's emotions. Classifying emotions in social media messages during crises including public health, social unrest, and natural disasters utilizing AI-powered supervised and unsupervised learning approaches. Context matters for sentiment analysis, and feature extraction and deep learning models improve accuracy. We discuss how real-time sentiment analysis may influence emergency personnel's communication, resource allocation, and crisis judgments. This paper provides case studies and comparative research to demonstrate how AI might help us act swiftly and confidently on community sentiment in crisis response. Our findings emphasize the need to keep studying sentiment analysis approaches to ensure they perform reliably in the turbulent social media landscape of events.