id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-032684-muh5rwla	Madichetty, Sreenivasulu	A stacked convolutional neural network for detecting the resource tweets during a disaster	2020-09-25		.txt	text/plain	6980	418	55	Specifically, the authors in [3] used both information-retrieval methodologies and classification methodologies (CNN with crisis word embeddings) to extract the Need and Availability of Resource tweets during the disaster. The main drawback of CNN with crisis embeddings is that it does not work well if the number of training tweets is small and, in the case of information retrieval methodologies, keywords must be given manually to identify the need and availability of resource tweets during the disaster. Initially, the experiment is performed on the SVM classifier based on the proposed domainspecific features for the identification of NAR tweets and compared to the BoW model shown in Table 5 . This paper proposes a method named as CKS (CNN and KNN are used as base-level classifiers, and SVM is used as a Meta-level classifier) for identifying tweets related to the Need and Availability of Resources during the disaster.	./cache/cord-032684-muh5rwla.txt	./txt/cord-032684-muh5rwla.txt