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Author Axel Schulz; Tung Dang Thanh; Heiko Paulheim; Immanuel Schweizer
Title A fine-grained sentiment analysis approach for detecting crisis related microposts Type Conference Article
Year 2013 Publication ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal ISCRAM 2013
Volume Issue Pages 846-851
Keywords 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
Abstract 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.
Address Technische Universität Darmstadt, Germany; Universität Mannheim, Germany
Corporate Author Thesis
Publisher Karlsruher Institut fur Technologie Place of Publication KIT; Baden-Baden Editor T. Comes, F. Fiedrich, S. Fortier, J. Geldermann and T. Müller
Language (down) English Summary Language English Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2411-3387 ISBN 9783923704804 Medium
Track Social Media Expedition Conference 10th International ISCRAM Conference on Information Systems for Crisis Response and Management
Notes Approved no
Call Number Serial 927
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