Crowd sentiment detection during disasters and crises
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|Conference paper (help)|
|Crowd sentiment detection during disasters and crises|
|Authors:||Ahmed Nagy, Jeannie Stamberger|
|Citation:||Proceedings of the 9th International ISCRAM Conference : 2012 April|
|Editors:||L. Rothkrantz, J. Ristvej, Z. Franco|
|Meeting:||9th International ISCRAM Conference|
|Database(s):||Google Scholar cites|
|Web:||DuckDuckGo Bing Google Yahoo! — Google PDF|
|Article:||Google Scholar PubMed|
|Restricted:||DTU Digital Library|
3698 tweets in regards the the "San Bruno event" in the first 24 hours. They used TwapperKeeper based on sanbrunofire keyword.
- The AFINN word list is misspelt as AFNN. In the code even AKNN.
- The AFINN word list is described as having values fromo -3 to +3. Actually the values are from -5 to +5.
- The conclusion is unclear. It states " Using Bayesian Networks with SentiWordNet yielded the best precision and recall" However the F-measure in Table 3 seems to show that the combination of AFINN, emoticons, SentiWordNet and the Bayesian network is the best.