Big data analytics for disaster response and recovery through sentiment analysis
By J. R. Ragini, P. Anand, V. Bhaskar. International Journal of Information Management, 2018
344 citations recorded by the source corpus.
Abstract Big data created by social media and mobile networks provide an exceptional opportunity to mine valuable insights from them. This information is harnessed by business entities to measure the level of customer satisfaction but its application in disaster response is still in its inflection point. Social networks are increasingly used for emergency communications and help related requests. During disaster situations, such emergency requests need to be mined from the pool of big data for providing timely help. Though government organizations and emergency responders work together through their respective national disaster response framework, the sentiment of the affected people during and after the disaster determines the success of the disaster response and recovery process. In this paper, we propose a big data driven approach for disaster response through sentiment analysis. The proposed model collects disaster data from social networks and categorize them according to the needs of the affected people. The categorized disaster data are classified through machine learning algorithm for analyzing the sentiment of the people. Various features like, parts of speech and lexicon are analyzed to identify the best classification strategy for disaster data. The results show that lexicon based approach is suitable for analyzing the needs of the people during disaster. The practical implication of the proposed methodology is the real-time categorization and classification of social media big data for disaster response and recovery. This analysis helps the emergency responders and rescue personnel to develop better strategies for effective information management of the rapidly changing disaster environment.
Subfields: Artificial Intelligence, Communication, Statistical and Nonlinear Physics
View Paper: https://www.semanticscholar.org/paper/70858b18a31b8e117fb51b8dd758f54ad1b149f1
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- John S. Edwards
- Yanqing Duan
- Yogesh Kumar Dwivedi
- Ashima Yadav
- Dinesh Kumar Vishwakarma
- Ali Abdallah Alalwan
In Communication
In Statistical and Nonlinear Physics
Citations in the corpus
- Towards Sustainable Energy: A Systematic Review of Renewable Energy Sources, Technologies, and Public Opinions (2019, background): “In this age of technology, opinions created by social networks provide an exceptional opportunity to mine valuable insights from them that can be used for the development of RES [31].”
- Sentiment analysis using deep learning architectures: a review (2019, background): “…al. (2017) and Singh et al. (2018) Crime prediction Wang et al. (2012b), Rosenfeld and Fornango (2008), Gerber (2014) and Azeez and Aravindhar (2015) Disaster assessment, response, management Ragini et al. (2018), Beigi et al. (2016), Radianti et al. (2016) and Wu and Cui (2018) as the sentiments.”
- Artificial intelligence for decision making in the era of Big Data - evolution, challenges and research agenda (2019, methodology): “The Big Data era has added types of data that were not previously used in analysis, such as that from social media (Martínez-Rojas, Pardo-Ferreira, & Rubio-Romero, 2018; Ragini et al., 2018).”