Abstract
Managing the risks of natural disasters can be enhanced by exploring social data. The need to swiftly extract meaningful information from large amounts of data generated by social network is on the rise especially to deal with natural disasters. New methods are needed to deeply support immersive social data analytics. Moreover, big data analysis seems to be able to improve accurate decisions to disaster management systems. The aim of this research is to determine critical cases and to focus on immersive sentiment analysis for big social data using Hadoop platform and machine learning technique. In one hand, we use MapReduce for the introduced data processing step. In the other hand, we apply support vector machine algorithm for the sentiment classification. We evaluate the performance of the performed classification method using the standard classification performance metrics accuracy, precision, recall, and F-measure and Microsoft Power BI as a visualization tool.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Velev, D., Zlateva, P.: Use of social media in natural disaster management. In: International Proceedings of Economic Development and Research, vol. 39, pp. 41–45 (2012)
Bilenko, M., Mooney, R.J.: Adaptive duplicate detection using learnable string similarity measures. In: Proceedings of the ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 39–48. ACM (2003)
Chen, Z., Kalashnikov, D.V., Mehrotra, S.: Exploiting context analysis for combining multiple entity resolution systems. In: Proceedings of the 2009 ACM SIGMOD International Conference on Management of Data, pp. 207–218. ACM (2009)
Gao, H., Barbier, G., Goolsby, R.: Harnessing the crowdsourcing power of social media for disaster relief. IEEE Intell. Syst. 26(3), 10–14 (2011)
Kongthon, A., et al.: The role of Twitter during a natural disaster: case study of 2011 Thai flood. In: 2012 Proceedings of Technology Management for Emerging Technologies (PICMET), PICMET 2012, pp. 2227–2232. IEEE (2012)
Yin, J., et al.: Using social media to enhance emergency situation awareness. IEEE Intell. Syst. 27(6), 52–59 (2012)
Dufty, N., et al.: Using social media to build community disaster resilience. Aust. J. Emerg. Manag. 27(1), 40 (2012)
Dugdale, J., Van de Walle, B., Koeppinghoff, C.: Social media and SMS in the Haiti earthquake. In: Proceedings of the 21st International Conference on World Wide Web, pp. 713–714. ACM (2012)
Heer, J., Agrawala, M.: Design considerations for collaborative visual analytics. Inf. Vis. 7(1), 49–62 (2008)
Reda, K., et al.: Visualizing large, heterogeneous data in hybrid-reality environments. IEEE Comput. Graph. Appl. 33(4), 38–48 (2013)
Dong, A., et al.: Time is of the essence: improving recency ranking using Twitter data. In: Proceedings of the 19th International Conference on World Wide Web, pp. 331–340. ACM (2010)
Beneito-Montagut, R., et al.: Governmental social media use for emergency communication. In: ISCRAM (2013)
Bharosa, N., Lee, J., Janssen, M.: Challenges and obstacles in sharing and coordinating information during multi-agency disaster response: propositions from field exercises. Inf. Syst. Front. 12(1), 49–65 (2010)
Pavlopoulos, S., Kyriacou, E., Berler, A., Dembeyiotis, S., Koutsouris, D.: A novel emergency telemedicine system based on wireless communication technology-ambulance. IEEE Trans. Inf Technol. Biomed. 2(4), 261–267 (1998)
Ahmed, Y.A., Ahmad, M.N., Zakaria, N.H.: Towards exploring factors that influence social media-based knowledge sharing intentions in disaster management. J. Theor. Appl. Inf. Technol. 88(3) (2016)
Yates, D., Paquette, S.: Emergency knowledge management and social media technologies: a case study of the 2010 Haitian earthquake. Int. J. Inf. Manag. 31(1), 6–13 (2011)
Cook, K.A., Thomas, J.J.: Illuminating the Path: The Research and Development Agenda for Visual Analytics (2005)
Chandler, T., et al.: Immersive analytics. In: Big Data Visual Analytics (BDVA), pp. 1–8. IEEE (2015)
Batrinca, B., Treleaven, P.C.: Social media analytics: a survey of techniques, tools and platforms. AI Soc. 30(1), 89–116 (2015)
Das, S., Chen, M.: Yahoo! for Amazon: extracting market sentiment from stock message boards. In: Proceedings of the Asia Pacific Finance Association Annual Conference (APFA), Bangkok, Thailand, vol. 35, p. 43 (2001)
Tong, R.M.: An operational system for detecting and tracking opinions in on-line discussion. In: Working Notes of the ACM SIGIR 2001 Workshop on Operational Text Classification, vol. 1, p. 6 (2001)
Kowcika, A., et al.: Sentiment analysis for social media. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 3(7), 1–6 (2013)
Bouali, H., Akaichi, J.: Comparative study of different classification techniques: heart disease use case. In: 2014 13th International Conference on Machine Learning and Applications (ICMLA), pp. 482–486. IEEE (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Toujani, R., Chaabani, Y., Dhouioui, Z., Bouali, H. (2018). The Next Generation of Disaster Management and Relief Planning: Immersive Analytics Based Approach. In: Beck, D., et al. Immersive Learning Research Network. iLRN 2018. Communications in Computer and Information Science, vol 840. Springer, Cham. https://doi.org/10.1007/978-3-319-93596-6_6
Download citation
DOI: https://doi.org/10.1007/978-3-319-93596-6_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-93595-9
Online ISBN: 978-3-319-93596-6
eBook Packages: Computer ScienceComputer Science (R0)