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Natural Language Processing to the Rescue? Extracting "Situational Awareness" Tweets during Mass Emergency
Published on 2011-08-184149 Views
In times of mass emergency, vast amounts of data are generated via computer-mediated communication (CMC) that are difficult to manually cull and organize into a coherent picture. Yet valuable infor
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Presentation
Natural Language Processing to the Rescue?00:00
Microblogging & Emergency00:16
Twitter & Emergency00:35
Situational Awareness01:50
Tweets during Mass Emergencies02:25
Tweets with Information Contributing to Situational Awareness (1)03:26
Tweets with Information Contributing to Situational Awareness (2)03:45
Tweets with Information Contributing to Situational Awareness (3)03:51
Hypothesis03:57
Goals04:19
Our Approach (1)04:38
Our Approach (2)04:46
Our Approach (3)04:53
Our Approach (4)05:00
Datasets05:10
2009 & 2010 Red River Floods05:38
2009 Oklahoma Fires06:12
2010 Haiti Earthquake06:41
Annotation06:57
Situational Awareness (SA) (1)07:32
Situational Awareness (SA) (2)08:10
Subjectivity (1)08:27
Subjectivity (2)09:02
Register (1)09:27
Register (2)10:01
Style (1)10:21
Style (2)11:01
Classification11:18
Classification -Technique11:45
Classification –Training data12:10
Classification - Overview12:19
Simple Features13:01
Linguistic Features13:34
Classification Results (1)14:06
SA Classification using Naïve Bayes and MaxEnt with all features14:21
Classification Results (2)15:10
MaxEnt Results on Balanced Dataset (1)15:39
MaxEnt Results on Balanced Dataset (2)16:39
MaxEnt Results on Balanced Dataset (3)16:56
MaxEnt Results on Balanced Dataset (4)17:19
MaxEnt Results on Balanced Dataset (5)17:27
MaxEnt Results on Balanced Dataset (6)17:50
MaxEnt Results on Balanced Dataset (7)17:57
MaxEnt Results on Balanced Dataset (8)18:04
Classification Results (3)18:09
MaxEnt Results on Balanced Dataset (9)18:24
MaxEntResults across all Events18:29
Conclusions19:52
Acknowledgements20:19