Multilabel classification of natural disaster news event using support vector machine / Nur Fatini Md Nazir

Nowadays, the ease of accessing online news through social media like Twitter is a concern for all generations. However, the problem is the process of verifying the accuracy of the information is time-consuming. Plus, the increment amount of online news stories makes it difficult to access disaster-...

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Main Author:	Md Nazir, Nur Fatini
Format:	Thesis
Language:	English
Published:	2023
Subjects:	Telecommunication
Online Access:	https://ir.uitm.edu.my/id/eprint/89017/1/89017.pdf
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id	my-uitm-ir.89017
record_format	uketd_dc
spelling	my-uitm-ir.890172024-03-19T07:07:38Z Multilabel classification of natural disaster news event using support vector machine / Nur Fatini Md Nazir 2023 Md Nazir, Nur Fatini Telecommunication Nowadays, the ease of accessing online news through social media like Twitter is a concern for all generations. However, the problem is the process of verifying the accuracy of the information is time-consuming. Plus, the increment amount of online news stories makes it difficult to access disaster-relevant news promptly. Therefore, to tackle these issues, a solution is proposed: a multilabel classification system using Support Vector Machine (SVM) for natural disaster news event and using TF-IDF for feature extraction method. The chosen methodology for this project is the waterfall model, known for its systematic and linear software development approach. The results demonstrate the promising outcomes, with SVM model achieving 65.2% accuracy with value C=3 as the parameter of the SVM model. Future work aims to expand the system’s capabilities to classify various types of natural disaster news event from social media, implement automatic classification with diverse techniques or models and extend the language support to include Malay. Incorporating techniques for spell-check, grammar correction and language quality assessment will enhance the system’s ability to accurately classify and extract information from natural disaster news event on social media. In summary, the proposed solution employs SVM-based multilabel classification with TF-IDF feature extraction to address challenges in accessing accurate disaster-relevant news from social media. The system showcases high accuracy and presents possibilities for further enhancements and language expansion. 2023 Thesis https://ir.uitm.edu.my/id/eprint/89017/ https://ir.uitm.edu.my/id/eprint/89017/1/89017.pdf text en public degree Universiti Teknologi MARA, Melaka College of Computing, Informatics and Mathematics
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
topic	Telecommunication
spellingShingle	Telecommunication Md Nazir, Nur Fatini Multilabel classification of natural disaster news event using support vector machine / Nur Fatini Md Nazir
description	Nowadays, the ease of accessing online news through social media like Twitter is a concern for all generations. However, the problem is the process of verifying the accuracy of the information is time-consuming. Plus, the increment amount of online news stories makes it difficult to access disaster-relevant news promptly. Therefore, to tackle these issues, a solution is proposed: a multilabel classification system using Support Vector Machine (SVM) for natural disaster news event and using TF-IDF for feature extraction method. The chosen methodology for this project is the waterfall model, known for its systematic and linear software development approach. The results demonstrate the promising outcomes, with SVM model achieving 65.2% accuracy with value C=3 as the parameter of the SVM model. Future work aims to expand the system’s capabilities to classify various types of natural disaster news event from social media, implement automatic classification with diverse techniques or models and extend the language support to include Malay. Incorporating techniques for spell-check, grammar correction and language quality assessment will enhance the system’s ability to accurately classify and extract information from natural disaster news event on social media. In summary, the proposed solution employs SVM-based multilabel classification with TF-IDF feature extraction to address challenges in accessing accurate disaster-relevant news from social media. The system showcases high accuracy and presents possibilities for further enhancements and language expansion.
format	Thesis
qualification_level	Bachelor degree
author	Md Nazir, Nur Fatini
author_facet	Md Nazir, Nur Fatini
author_sort	Md Nazir, Nur Fatini
title	Multilabel classification of natural disaster news event using support vector machine / Nur Fatini Md Nazir
title_short	Multilabel classification of natural disaster news event using support vector machine / Nur Fatini Md Nazir
title_full	Multilabel classification of natural disaster news event using support vector machine / Nur Fatini Md Nazir
title_fullStr	Multilabel classification of natural disaster news event using support vector machine / Nur Fatini Md Nazir
title_full_unstemmed	Multilabel classification of natural disaster news event using support vector machine / Nur Fatini Md Nazir
title_sort	multilabel classification of natural disaster news event using support vector machine / nur fatini md nazir
granting_institution	Universiti Teknologi MARA, Melaka
granting_department	College of Computing, Informatics and Mathematics
publishDate	2023
url	https://ir.uitm.edu.my/id/eprint/89017/1/89017.pdf
_version_	1794192188534423552

Multilabel classification of natural disaster news event using support vector machine / Nur Fatini Md Nazir

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