OPTIMIZING EMERGENCY RESPONSES VIA DISASTER LSTM ALGORITHM FOR SOCIAL MEDIA ANALYSIS

The increasing frequency of natural disasters demands efficient and accurate classification systems for real-time information dissemination. This study develops and evaluates classification systems for tweets related to six disaster types—hurricane, forest fire, tornado, drought, flood, and fire—aiming to enhance disaster response capabilities through improved classification accuracy and sentiment analysis. The study uses a dataset of labeled tweets, incorporating both disaster type and sentiment labels (positive, negative, neutral) to assess public reactions. By integrating sentiment analysis into the classification process, emergency responders can identify disaster events and gauge the emotional tone of the population in real-time, providing crucial insights for better decision-making. Two models were implemented: a K-Nearest Neighbors (KNN) algorithm and a Disaster Long Short-Term Memory (DLSTM) network. The KNN algorithm, used as a baseline, showed adequate performance but struggled with the complexities of sequential text data and sentiment analysis. In contrast, the Disaster LSTM model, which utilized both disaster classification and sentiment analysis, significantly outperformed KNN. It achieved an overall accuracy of 84.0%, with 87.2% for hurricanes and 81.9% for droughts, while also demonstrating strong performance in sentiment classification. The novelty of this study lies in its dual-focus model, which simultaneously classifies disaster types and associated sentiments using a fine-tuned LSTM network. This integrated approach offers a more comprehensive understanding of real-time public discourse during disasters—unlike prior studies that addressed these aspects in isolation. These improvements enable faster disaster identification, more efficient resource allocation, and a better understanding of public emotions during crises. This research contributes to the fields of natural language processing and disaster management by constructing a robust framework for leveraging social media data for real-time situational awareness and emergency response.