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- W3204409957 abstract "Social media is an essential source of information during a disaster. The number of posts is increased on social media during a disaster. It includes infrastructure damage, availability, requirement of resources and also non-related to the target class. Detecting the target class such as availability and requirement of resources is a challenging task among the posts. Most of the prior works focused on detection of the earthquake events during a disaster. However, it includes more classes and they are not focused on the specifically availability and requirement of resources. To address this problem, re-ranking feature selection algorithm is proposed using χ2-statistic feature selection and maximum frequency for detecting the availability and requirement of resources. It is tested on the real-time Nepal earthquake tweet dataset. The proposed re-ranking feature selection algorithm with standard baselines such as BOW model, χ2-statistic feature selection and Maximum frequency is compared. The proposed re-ranking feature selection algorithm outperforms the standard baseline in terms of precision for detecting the availability and requirement of resources during a disaster." @default.
- W3204409957 created "2021-10-11" @default.
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- W3204409957 date "2018-01-01" @default.
- W3204409957 modified "2023-09-23" @default.
- W3204409957 title "Re-Ranking Feature Selection Algorithm for Detecting the Availability and Requirement of Resources Tweets during Disaster" @default.
- W3204409957 hasPublicationYear "2018" @default.
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