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.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. | |
| dc.identifier.uri | https://hdl.handle.net/1920/12213 | |
| dc.rights | Attribution-NonCommercial-NoDerivs 3.0 United States | |
| dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/3.0/us/ | |
| dc.subject | Machine learning | |
| dc.subject | Social media | |
| dc.subject | Natural language processing | |
| dc.title | Real Time Sentiment Analysis of Online Information for Fast Emergency Response | |
| dc.type | Working Paper |