id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-322337-4xhwm3k4	Desai, P. S.	Sentiment Informed Timeseries Analyzing AI (SITALA) to curb the spread of COVID-19 in Houston	2020-07-24		.txt	text/plain	1760	135	61	The present study introduces a new AI model, viz., Sentiment Informed Timeseries Analyzing AI (SITALA), that has been trained on COVID-19 test positivity data and news sentiment from over 2750 news articles for the Harris county. The model forecasts that in order to curb the spread of coronavirus in Houston, a sustained negative news sentiment will be desirable. This study attempts to develop a multivariate artificial intelligence (AI) model to analyze timeseries of COVID-19 positivity and news sentiment. The AI model is inspired by Google's Wavenet (11) architecture and uses IBM Watson Discovery News (12) to mine COVID-19 sentiment in the news articles. The COVID-19 test positivity data for Harris county was obtained from the website of Texas Department of State Health Services (https://dshs.texas.gov/coronavirus/additionaldata.aspx). SITALA forecast (gray window) shows how maintaining a negative sentiment in the news about the spread of COVID-19 can be beneficial to control and 15 eventually decrease test positivity.	./cache/cord-322337-4xhwm3k4.txt	./txt/cord-322337-4xhwm3k4.txt