Using Opinion Mining Techniques for Early Crisis Detection
Keywords:
Opinion mining, Event detection, Crisis managementAbstract
The goal of our research is to investigate the use of internet monitoring in crisis management using linguistic processing and text mining techniques. We present a system that detects and classifies events on topics and, using an altered opinion mining workflow, detects geographical entities related to these events and the sentiments expressed towards them. The results are displayed in customized GoogleMaps views, indicating areas with a potential risk, such as natural disasters, unfavorable weather or threatening protests. All the processing is done in real time and, depending on the monitored sources, our work could be of used as a population warning system, but it could also be useful for regional or local authorities in managing intervention time and resources by prioritizing the situations for which they have to act.
References
D. Blei, J. Lafferty, Topic models, Text Mining:Theory and Applications, Taylor and Francis, London, UK, 2009.
D. Blei, A. Ng, M. Jordan, Latent Dirichlet allocation, Journal of Machine Learning Research, 3:993-1022, 2003.
A. L. Ginsca, et al., Sentimatrix - Multilingual Sentiment Analysis Service, In Proceedings of the 2nd Workshop ACL-WASSA, 2011.
A. Iftene, D. Trandabat, M. Toader, M. Corici, Named Entity Recognition for Romanian. In Knowledge Engineering, Principles and Techniques. Selected Papers, 49-60, 2011.
F. Johansson, et al., Detecting Emergent Conflicts through Web Mining and Visualization, In Proceedings of the European Intelligence and Security Informatics Conference, 2011.
K. Lang, Newsweeder: Learning to filter netnews, Proceedings of the Twelfth International Conference on Machine Learning, pages 331-339, 1995
D. Lin, An information-theoretic definition of similarity, In Proceedings of the International Conference on Machine Learning, Madison, August, 1998.
B. Pang, L. Lee, Opinion mining and sentiment analysis, Foundations and Trends in Information Retrieval, 2(1-2), 1-135, 2008. http://dx.doi.org/10.1561/1500000011
P. Resnik, Using information content to evaluate semantic similarity, In Proceedings of the 14th International Joint Conference, 1995.
Z. Wu, M. Palmer, Verb semantics and lexical selection, In 32nd Annual Meeting of the Association for Computational Linguistics, pages 133-138, Las Cruces, New Mexico, 1994. http://dx.doi.org/10.3115/981732.981751
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