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A fine-grained sentiment analysis approach for detecting crisis related microposts
Axel Schulz
author
Tung Dang Thanh
author
Heiko Paulheim
author
Immanuel Schweizer
author
2013
Karlsruher Institut fur Technologie
KIT; Baden-Baden
English
Real-time information from microposts like Twitter is useful for applications in the crisis management domain. Currently, that potentially valuable information remains mostly unused by the command staff, mainly because the sheer amount of information cannot be handled efficiently. Sentiment analysis has been shown as an effective tool to detect microposts (such as tweets) that contribute to situational awareness. However, current approaches only focus on two or three emotion classes. But using only tweets with negative emotions for crisis management is not always sufficient. The amount of remaining information is still not manageable or most of the tweets are irrelevant. Thus, a more fine-grained differentiation is needed to identify relevant microposts. In this paper, we show the systematic evaluation of an approach for sentiment analysis on microposts that allows detecting seven emotion classes. A preliminary evaluation of our approach in a crisis related scenario demonstrates the applicability and usefulness.
Artificial intelligence
Information systems
Learning systems
Risk management
Social networking (online)
Amount of information
Emergency management
Microposts
Real-time information
Sentiment analysis
Situational awareness
Systematic evaluation
Twitter
Data mining
exported from refbase (http://idl.iscram.org/show.php?record=927), last updated on Sun, 09 Aug 2015 05:41:39 +0200
text
http://idl.iscram.org/files/schulz/2013/927_Schulz_etal2013.pdf
AxelSchulz_etal2013
ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management
ISCRAM 2013
T. Comes
F
Fiedrich
editor
10th International ISCRAM Conference on Information Systems for Crisis Response and Management
2013
Karlsruher Institut fur Technologie
KIT; Baden-Baden
conference publication
846
851
9783923704804
2411-3387
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