The Sentiment Analysis of Bekasi Floods Using SVM and Naive Bayes with Advanced Feature Selection

Abstract

Flood management in Bekasi City poses significant challenges, necessitating strategies grounded in an understanding of community sentiment. This study aims to develop and optimize sentiment analysis of social media data related to flooding using Support Vector Machine (SVM) and advanced feature selection techniques. The primary goal is to enhance the accuracy of classifying public sentiment toward flood management efforts in Bekasi City. Data is collected from various social media platforms, preprocessed, and analyzed using SVM with feature selection techniques like Information Gain and Analysis of Variance (ANOVA). (Thoriq et al., 2023) Our findings indicate that using SVM with advanced feature selection significantly improves sentiment classification accuracy compared to standard methods. These results offer insights into public perceptions, helping policymakers improve management strategies and communication for flood events. This method assists in understanding community responses and pinpointing critical areas needing attention. Moreover, this study contributes to disaster management in urban flood-prone areas by presenting a methodological approach applicable to other disaster contexts. Integrating social media sentiment analysis with advanced machine learning techniques offers a robust framework for real-time public sentiment assessment, enhancing disaster response strategies. Furthermore, these techniques help create a more resilient urban environment by improving the efficiency and effectiveness of flood management practices. This comprehensive tool is essential for better preparedness, response, and recovery from flood events, ultimately enhancing community resilience and safety in Bekasi City. This research is part of machine learning in disaster management and a valuable asset for city planners and disaster professionals around the world.