Abstract
Social media has assumed a huge part in scattering data about these disasters by permitting individuals to share data and request help. During disaster, social media gives a plenty of data which incorporates data about the idea of disaster, impacted individuals’ feelings and aid ventures. This data proliferated over the social media can save great many life by alarming others, so they can make a hesitant move. Numerous offices are attempting to automatically dissect tweets and perceive disasters and crises. This sort of work can be advantageous to a great many individuals associated with the Web, who can be alarmed on account of a crises or disaster. Twitter information is unstructured information; in this manner, natural language processing (NLP) must be performed on the Twitter information to arrange them into classes–“Connected with Disaster” and “Not connected with Disaster.” The paper does an expectation on the test set made from the first informational collection. It does an exactness testing of the classifier model created. This paper involves Naive Bayes classification mechanism for building the classifier model and for making predictions.
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Sharma, A., Thakur, K., Kapoor, D.S., Singh, K.J., Saroch, T., Kumar, R. (2023). Disaster Analysis Through Tweets. In: Kumar, S., Sharma, H., Balachandran, K., Kim, J.H., Bansal, J.C. (eds) Third Congress on Intelligent Systems. CIS 2022. Lecture Notes in Networks and Systems, vol 608. Springer, Singapore. https://doi.org/10.1007/978-981-19-9225-4_40
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