id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-318879-4ual2ssa	Kaveh-Yazdy, Fatemeh	Track Iran's National COVID-19 Response Committee’s Major Concerns using Two-stage Unsupervised Topic Modeling	2020-11-04		.txt	text/plain	6255	383	57	title: Track Iran's National COVID-19 Response Committee's Major Concerns using Two-stage Unsupervised Topic Modeling The topic modeling and tracking are utilized in a two-stage framework, which is customized for this problem to separate miscellaneous sentences from those presenting concerns. The remained sentences are vectorized, adopting Tf-IDF weighting schema in the second stage and topically modeled by the LDA method. Disease-related text mining researches with respect to their application can be divided into four primary groups as follows, 1-Outbreak monitoring and prediction 2-Infodemic and misinformation detection 3-Social/public concern detection 4-Control Disease Centers response analyzing We collect news posts, including quotes made by members of the NCRC, and then group them to select a major part of the sentences covering similar topics. In this article, we used a two-stage framework to group, select, and cluster the sentences expressing concerns of Iran's NCRC.	./cache/cord-318879-4ual2ssa.txt	./txt/cord-318879-4ual2ssa.txt