Neural Network-Based Sentiment Analysis and Anomaly Detection in Crisis-Related Tweets

During crises, people use X to share real-time updates. These posts reveal public sentiment and evolving emergency situations. However, the changing sentiment in tweets coupled with anomalous patterns may indicate significant events, misinformation or emerging hazards that require timely detection. By using a neural network, and employing deep learning techniques for crisis observation, this study proposes a pipeline for sentiment analysis and anomaly detection in crisis-related tweets. The authors used pre-trained BERT to classify tweet sentiment. For sentiment anomaly detection, autoencoders and recurrent neural networks (RNNs) with an attention mechanism were applied to capture sequential relationships and identify irregular sentiment patterns that deviate from standard crisis talk. Experimental results show that neural networks are more accurate than traditional machine learning methods for both sentiment categorization and anomaly detection tasks, with higher precision and recall for identifying sentiment shifts in the public. This study indicates that neural networks can be used for crisis management and the early detection of significant sentiment anomalies. This could be beneficial to emergency responders and policymakers and support data-driven decisions.