Resource management is an essential task that needs to be performed by the government or any disaster management agency during natural disasters. During these critical circumstances, people mostly depend upon a social media platform to share and collect information about the situation of the affected localities. The huge volume of real-time data can be useful in disaster assessment, response, and relief activities. We have presented a system which analyzes tweets during natural disasters and categorizes them according to the availability or need for general or medical resources along with their location information (if any) mentioned in the tweets. Several statistical classifiers are applied to show their usefulness for a better solution. Optimal feature representation is the heart of any machine learning based classification model. Here, we have applied a forest optimization-based wrapper feature selection algorithm to improve the classification accuracy. FIRE, SMERP, and CrisisLex dataset are used to evaluate our system and its effectiveness is demonstrated for smooth management of the resources. From the experimentation, it is found that forest optimization algorithm (FOA) wrapped multinomial naive bayes classifier gives an accuracy of 91.41 percent and f-measure of 88.33 percent on the FIRE dataset. The execution time of the model is quite less which will be very helpful for this challenging task.

Mining social media text for disaster resource management using a feature selection based on forest optimization

Narducci Fabio;
2022

Abstract

Resource management is an essential task that needs to be performed by the government or any disaster management agency during natural disasters. During these critical circumstances, people mostly depend upon a social media platform to share and collect information about the situation of the affected localities. The huge volume of real-time data can be useful in disaster assessment, response, and relief activities. We have presented a system which analyzes tweets during natural disasters and categorizes them according to the availability or need for general or medical resources along with their location information (if any) mentioned in the tweets. Several statistical classifiers are applied to show their usefulness for a better solution. Optimal feature representation is the heart of any machine learning based classification model. Here, we have applied a forest optimization-based wrapper feature selection algorithm to improve the classification accuracy. FIRE, SMERP, and CrisisLex dataset are used to evaluate our system and its effectiveness is demonstrated for smooth management of the resources. From the experimentation, it is found that forest optimization algorithm (FOA) wrapped multinomial naive bayes classifier gives an accuracy of 91.41 percent and f-measure of 88.33 percent on the FIRE dataset. The execution time of the model is quite less which will be very helpful for this challenging task.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/4806253
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