Real Time Sentiment Analysis of Online Information for Fast Emergency Response

dc.contributor.author	Lewis, Michael
dc.contributor.author	Tabassum, Munira
dc.contributor.author	Bibhuti, Reeti
dc.date.accessioned	2022-01-19T21:28:42Z
dc.date.available	2022-01-19T21:28:42Z
dc.date.issued	2021-04-28
dc.identifier.uri	http://hdl.handle.net/1920/12213
dc.description.abstract	Semantic analysis has been widely researched in the domain of online review sites with the aim of generating summarized opinions of users about different aspects of products. Analyzing such semantics from online social networking sites can help emergency responders understand the dynamics of the network. In this paper, we perform an analysis of tweets posted on Twitter during the disastrous Hurricane Ida and create dashboards based on extracted semantic metadata. The research and development of this product seeks to address the lag times between disaster and disaster response.	en_US
dc.rights	Attribution-NonCommercial-NoDerivs 3.0 United States	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/us/	*
dc.subject	machine learning	en_US
dc.subject	social media	en_US
dc.subject	natural language processing	en_US
dc.title	Real Time Sentiment Analysis of Online Information for Fast Emergency Response	en_US
dc.type	Working Paper	en_US