A Comparative Analysis of Machine Learning Algorithms for Classification of Natural Disaster Tweets

This paper proposes a system which helps in categorizing disaster-related tweets, using a diverse dataset representing various disaster types and categories. The process deals with pre-processing, feature engineering and ultimately the model selection to develop precise classification model. The developed model's performance is evaluated with various parameters and via rigorous testing. The system has significant implications for disaster response and management, enabling timely and targeted interventions. By analyzing tweet content, the model can identify specific disaster types, facilitating effective resource allocation. The study's findings highlight the importance of social media in disaster management and response. The proposed system can be integrated with the existing disaster management frameworks, which ultimately enhance their effectiveness. Experimental results demonstrate the model's accuracy and robustness in categorizing disaster-related tweets.