Abstract
Existing studies on Twitter-based natural disaster analysis suffer from shortcomings like limitations on supported languages, lack of sentiment analysis, regional restrictions, lack of end-to-end automation, and lack of Mobile App support. In this study, we design and develop a fully-automated artificial intelligence (AI) based Decision Support System (DSS) available through multiple platforms like iOS, Android, and Windows. The proposed DSS uses a live Twitter feed to obtain natural disaster-related Tweets in 110 supported languages. The system automatically executes AI-based translation, sentiment analysis, and automated K-Means algorithm to generate AI-driven insights for disaster strategists. The proposed DSS was tested with 67,528 real-time Tweets captured between 28 September 2021 and 6 October 2021 in 39 different languages under two different scenarios. The system revealed critical information for disaster planners or strategists like which clusters of natural disasters were associated with the most negative sentiments. We evaluated the proposed system’s accuracy and user experiences from 12 different disaster strategists. 83.33% of users found the proposed solution easy to use, effective, and self-explanatory. With 97% and 99.7% accuracy in Twitter keyword extraction and entity classification, this DSS reported the most accurate disaster intelligence system on a mobile platform.
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