Disaster Analysis Through Tweets

Anshul Sharma; Khushal Thakur; Divneet Singh Kapoor; Kiran Jot Singh; Tarun Saroch; Raj Kumar

doi:10.1007/978-981-19-9225-4_40

Disaster Analysis Through Tweets

Anshul Sharma, Khushal Thakur, Divneet Singh Kapoor, Kiran Jot Singh, Tarun Saroch, Raj Kumar

Chandigarh University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

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.

Original language	English
Title of host publication	3rd Congress on Intelligent Systems - Proceedings of CIS 2022
Editors	Sandeep Kumar, Harish Sharma, K. Balachandran, Joong Hoon Kim, Jagdish Chand Bansal
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	543-554
Number of pages	12
ISBN (Print)	9789811992247
DOIs	https://doi.org/10.1007/978-981-19-9225-4_40
State	Published - 2023
Externally published	Yes
Event	3rd Congress on Intelligent Systems, CIS 2022 - Bengaluru, India Duration: 5 Sep 2022 → 6 Sep 2022

Publication series

Name	Lecture Notes in Networks and Systems
Volume	608
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	3rd Congress on Intelligent Systems, CIS 2022
Country/Territory	India
City	Bengaluru
Period	5/09/22 → 6/09/22

Bibliographical note

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Keywords

Classifier model
Naive Bayes classification
Natural language processing
Twitter analysis

Access to Document

10.1007/978-981-19-9225-4_40

Cite this

Sharma, A., Thakur, K., Kapoor, D. S., Singh, K. J., Saroch, T., & Kumar, R. (2023). Disaster Analysis Through Tweets. In S. Kumar, H. Sharma, K. Balachandran, J. H. Kim, & J. C. Bansal (Eds.), 3rd Congress on Intelligent Systems - Proceedings of CIS 2022 (pp. 543-554). (Lecture Notes in Networks and Systems; Vol. 608). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-19-9225-4_40

Sharma, Anshul ; Thakur, Khushal ; Kapoor, Divneet Singh et al. / Disaster Analysis Through Tweets. 3rd Congress on Intelligent Systems - Proceedings of CIS 2022. editor / Sandeep Kumar ; Harish Sharma ; K. Balachandran ; Joong Hoon Kim ; Jagdish Chand Bansal. Springer Science and Business Media Deutschland GmbH, 2023. pp. 543-554 (Lecture Notes in Networks and Systems).

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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{\textquoteright} 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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author = "Anshul Sharma and Khushal Thakur and Kapoor, {Divneet Singh} and Singh, {Kiran Jot} and Tarun Saroch and Raj Kumar",

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year = "2023",

doi = "10.1007/978-981-19-9225-4_40",

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Sharma, A, Thakur, K, Kapoor, DS, Singh, KJ, Saroch, T & Kumar, R 2023, Disaster Analysis Through Tweets. in S Kumar, H Sharma, K Balachandran, JH Kim & JC Bansal (eds), 3rd Congress on Intelligent Systems - Proceedings of CIS 2022. Lecture Notes in Networks and Systems, vol. 608, Springer Science and Business Media Deutschland GmbH, pp. 543-554, 3rd Congress on Intelligent Systems, CIS 2022, Bengaluru, India, 5/09/22. https://doi.org/10.1007/978-981-19-9225-4_40

Disaster Analysis Through Tweets. / Sharma, Anshul; Thakur, Khushal; Kapoor, Divneet Singh et al.
3rd Congress on Intelligent Systems - Proceedings of CIS 2022. ed. / Sandeep Kumar; Harish Sharma; K. Balachandran; Joong Hoon Kim; Jagdish Chand Bansal. Springer Science and Business Media Deutschland GmbH, 2023. p. 543-554 (Lecture Notes in Networks and Systems; Vol. 608).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Disaster Analysis Through Tweets

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

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Cite this