Sentiment analysis on COVID-19 outbreak using PSO-SVM / Amir Danial Shahrul Sazali

The COVID-19 pandemic has had a huge influence on worldwide society, resulting in widespread lockdowns and considerable changes in everyday life. This project provides the analyzation of attitudes expressed in textual data connected to the COVID-19 outbreak using Particle Swarm Optimization with Sup...

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Main Author:	Shahrul Sazali, Amir Danial
Format:	Thesis
Language:	English
Published:	2024
Subjects:	Expert systems (Computer science) Fuzzy expert systems
Online Access:	https://ir.uitm.edu.my/id/eprint/96310/1/96310.pdf
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id	my-uitm-ir.96310
record_format	uketd_dc
spelling	my-uitm-ir.963102024-06-04T07:20:33Z Sentiment analysis on COVID-19 outbreak using PSO-SVM / Amir Danial Shahrul Sazali 2024 Shahrul Sazali, Amir Danial Expert systems (Computer science). Fuzzy expert systems The COVID-19 pandemic has had a huge influence on worldwide society, resulting in widespread lockdowns and considerable changes in everyday life. This project provides the analyzation of attitudes expressed in textual data connected to the COVID-19 outbreak using Particle Swarm Optimization with Support Vector Machines (SVM). This project is driven by the objectives to identify the requirement of Particle Swarm Optimization with Support Vector Machines (PSO-SVM) in sentiment analysis of covid-19 tweets, to apply the PSO-SVM method for sentiment analysis that classified tweets accurately and to evaluate the result of the PSO-SVM model for Covid-19 outbreak sentiment analysis. PSO is an optimization technique by searching decision space by sharing global information between different particles. SVM is a supervised learning model that looks at data for classification by searching hyperplane between classes. The created model achieves 73% accuracy in predicting sentiment of tweets when using a Linear SVM kernel with 70:30 percentage split ratio. The project is set to be improved by using a well-constructed SVM algorithm that can handle large data very well, using a more powerful hardware and unlimiting the language use to train the PSO-SVM. 2024 Thesis https://ir.uitm.edu.my/id/eprint/96310/ https://ir.uitm.edu.my/id/eprint/96310/1/96310.pdf text en public degree Universiti Teknologi MARA, Terengganu Faculty of Computer and Mathematical Sciences Jantan, Hamidah
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Jantan, Hamidah
topic	Expert systems (Computer science) Fuzzy expert systems
spellingShingle	Expert systems (Computer science) Fuzzy expert systems Shahrul Sazali, Amir Danial Sentiment analysis on COVID-19 outbreak using PSO-SVM / Amir Danial Shahrul Sazali
description	The COVID-19 pandemic has had a huge influence on worldwide society, resulting in widespread lockdowns and considerable changes in everyday life. This project provides the analyzation of attitudes expressed in textual data connected to the COVID-19 outbreak using Particle Swarm Optimization with Support Vector Machines (SVM). This project is driven by the objectives to identify the requirement of Particle Swarm Optimization with Support Vector Machines (PSO-SVM) in sentiment analysis of covid-19 tweets, to apply the PSO-SVM method for sentiment analysis that classified tweets accurately and to evaluate the result of the PSO-SVM model for Covid-19 outbreak sentiment analysis. PSO is an optimization technique by searching decision space by sharing global information between different particles. SVM is a supervised learning model that looks at data for classification by searching hyperplane between classes. The created model achieves 73% accuracy in predicting sentiment of tweets when using a Linear SVM kernel with 70:30 percentage split ratio. The project is set to be improved by using a well-constructed SVM algorithm that can handle large data very well, using a more powerful hardware and unlimiting the language use to train the PSO-SVM.
format	Thesis
qualification_level	Bachelor degree
author	Shahrul Sazali, Amir Danial
author_facet	Shahrul Sazali, Amir Danial
author_sort	Shahrul Sazali, Amir Danial
title	Sentiment analysis on COVID-19 outbreak using PSO-SVM / Amir Danial Shahrul Sazali
title_short	Sentiment analysis on COVID-19 outbreak using PSO-SVM / Amir Danial Shahrul Sazali
title_full	Sentiment analysis on COVID-19 outbreak using PSO-SVM / Amir Danial Shahrul Sazali
title_fullStr	Sentiment analysis on COVID-19 outbreak using PSO-SVM / Amir Danial Shahrul Sazali
title_full_unstemmed	Sentiment analysis on COVID-19 outbreak using PSO-SVM / Amir Danial Shahrul Sazali
title_sort	sentiment analysis on covid-19 outbreak using pso-svm / amir danial shahrul sazali
granting_institution	Universiti Teknologi MARA, Terengganu
granting_department	Faculty of Computer and Mathematical Sciences
publishDate	2024
url	https://ir.uitm.edu.my/id/eprint/96310/1/96310.pdf
_version_	1804889984755302400

Sentiment analysis on COVID-19 outbreak using PSO-SVM / Amir Danial Shahrul Sazali

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