International Journal of Computer and Applications

Sentiment analysis on Twitter: A text mining approach to the Syrian refugee crisis

https://hal-audencia.archives-ouvertes.fr/hal-01996486/document

Big data analytics and organizational culture as complements to swift trust and collaborative performance in the humanitarian supply chain

The era of big data and analytics is opening up new possibilities for disaster management (DM). Due to its ability to visualize, analyze and predict disasters, big data is changing the humanitarian operations and crisis management dramatically. Yet, the relevant literature is diverse and fragmented, which calls for its review in order to ascertain its development. A number of publications have dealt with the subject of big data and its applications for minimizing disasters. Based on a systematic literature review, this study examines big data in DM to present main contributions, gaps, challenges and future research agenda. The study presents the findings in terms of yearly distribution, main journals, and most cited papers. The findings also show a classification of publications, an analysis of the trends and the impact of published research in the DM context. Overall the study contributes to a better understanding of the importance of big data in disaster management.

/pdf/big-data-and-disaster-management-a-systematic-review-and-3gsgopeqcz.pdf

Big data and disaster management: a systematic review and agenda for future research

Sentiment analysis of Twitter data during critical events through Bayesian networks classifiers

Thousands of human lives are lost every year around the globe, apart from significant damage on property, animal life, etc., due to natural disasters (e.g., earthquake, flood, tsunami, hurricane and other storms, landslides, cloudburst, heat wave, forest fire). In this paper, we focus on reviewing the application of data mining and analytical techniques designed so far for (i) prediction, (ii) detection, and (iii) development of appropriate disaster management strategy based on the collected data from disasters. A detailed description of availability of data from geological observatories (seismological, hydrological), satellites, remote sensing and newer sources like social networking sites as twitter is presented. An extensive and in-depth literature study on current techniques for disaster prediction, detection and management has been done and the results are summarized according to various types of disasters. Finally a framework for building a disaster management database for India hosted on open source Big Data platform like Hadoop in a phased manner has been proposed. The study has special focus on India which ranks among top five counties in terms of absolute number of the loss of human life.

A review on application of data mining techniques to combat natural disasters

Graph-based approaches of feature selection have found their application in diverse areas owing to their efficacy to detect potential connections among the features. In this paper, we propose a hybrid graph-based approach for selecting a subset of features in the context of supervised learning. The novelty of this approach lies in the utilisation of graph-theoretic concepts of degree centrality as well as eigenvector centrality to assess the relative importance of features. Additionally, entropy-based measures have been used. The performance of the proposed approach is compared to that of the existing benchmark algorithms using publicly available datasets. The results have come out to be quite encouraging.

Saptarsi Goswami

Papers

A review on application of data mining techniques to combat natural disasters

A Hybrid Graph Centrality Based Feature Selection Approach for Supervised Learning