id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-329986-sbyu7yuc	Farrokhi, Aydin	Using artificial intelligence to detect crisis related to events: Decision making in B2B by artificial intelligence	2020-11-30		.txt	text/plain	10464	540	48	The study extends the situational crisis communication theory (SCCT) and Attribution theory frameworks built on big data and machine learning capabilities for early detection of crises in the market. This pioneering study is among the first studies that endeavour to use email data and sentiment analysis for extracting meaningful information that helps early detection of a crisis in an organization. This study aims to develop a big data analytics framework by deploying artificial intelligence rational agents generated by R/Python programming language capable of collecting data from different sources, such as emails, Tweets, Facebook, weblogs, online communities, databases, and documents, among others (structured, semistructured, and unstructured data). Previous studies have considered the use of network data for situational awareness; however, to the authors' knowledge, none have specifically investigated or analyzed the use of email communication by major organizations for situational assessment of a developing crisis.	./cache/cord-329986-sbyu7yuc.txt	./txt/cord-329986-sbyu7yuc.txt